Package 'crmPack' reference manual

Title:	Object-Oriented Implementation of Dose Escalation Designs
Description:	Implements a wide range of dose escalation designs. The focus is on model-based designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy to setup a new design with custom JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanés Bové et al. (2019) <doi:10.18637/jss.v089.i10>.
Authors:	Daniel Sabanés Bové [aut, cre] (ORCID: <https://orcid.org/0000-0002-0176-9239>), Wai Yin Yeung [aut], Burak Kuersad Guenhan [aut], Giuseppe Palermo [aut], Thomas Jaki [aut], Jiawen Zhu [aut], Ziwei Liao [aut], Dimitris Kontos [aut], Marlene Schulte-Goebel [aut], Doug Kelkhoff [aut] (ORCID: <https://orcid.org/0009-0003-7845-4061>), Oliver Boix [aut], Robert Adams [aut], Clara Beck [aut], John Kirkpatrick [aut], Wojciech Wójciak [aut], Guanya Peng [aut], Prerana Chandratre [aut], F. Hoffmann-La Roche AG [cph, fnd], Merck Healthcare KGaA [cph, fnd], Bayer AG [cph, fnd], RPACT GmbH [cph, fnd]
Maintainer:	Daniel Sabanés Bové <[email protected]>
License:	GPL (>= 2)
Version:	2.1.0.9000
Built:	2026-05-16 05:22:16 UTC
Source:	https://github.com/Roche/crmPack

`CohortSize`

Description

CohortSize is a class for cohort sizes.

Usage

.DefaultCohortSize()

.DefaultCohortSize()
.DefaultCohortSize()

.DefaultCohortSize()

Note

Typically, end users will not use the DefaultCohortSize() function.

Combine Two Stopping Rules with AND

Description

The method combining two atomic stopping rules.

Usage

## S4 method for signature 'Stopping,Stopping'
e1 & e2
## S4 method for signature 'Stopping,Stopping'
e1 & e2

Arguments

e1

(Stopping)
first stopping rule object.

e2

(Stopping)
second stopping rule object.

Value

The StoppingAll object.

Examples

## Example of combining two atomic stopping rules with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping <- myStopping1 & myStopping2
## Example of combining two atomic stopping rules with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping <- myStopping1 & myStopping2

Combine an Atomic Stopping Rule and a Stopping List with AND

Description

The method combining an atomic stopping rule and a stopping list.

Usage

## S4 method for signature 'Stopping,StoppingAll'
e1 & e2
## S4 method for signature 'Stopping,StoppingAll'
e1 & e2

Arguments

e1

(Stopping)
stopping rule object.

e2

(StoppingAll)
stopping list object.

Value

The modified StoppingAll object.

Examples

## Example of combining an atomic stopping rule with a list of stopping rules
## with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping3 <- StoppingMinPatients(nPatients = 20)

myStopping <- myStopping3 & (myStopping1 | myStopping2)
## Example of combining an atomic stopping rule with a list of stopping rules
## with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping3 <- StoppingMinPatients(nPatients = 20)

myStopping <- myStopping3 & (myStopping1 | myStopping2)

Combine a Stopping List and an Atomic Stopping Rule with AND

Description

The method combining a stopping list and an atomic stopping rule.

Usage

## S4 method for signature 'StoppingAll,Stopping'
e1 & e2
## S4 method for signature 'StoppingAll,Stopping'
e1 & e2

Arguments

e1

(StoppingAll)
stopping list object.

e2

(Stopping)
stopping rule object.

Value

The modified StoppingAll object.

Examples

## Example of combining a list of stopping rules with an atomic stopping rule
## with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping3 <- StoppingMinPatients(nPatients = 20)

myStopping <- (myStopping1 | myStopping2) & myStopping3
## Example of combining a list of stopping rules with an atomic stopping rule
## with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping3 <- StoppingMinPatients(nPatients = 20)

myStopping <- (myStopping1 | myStopping2) & myStopping3

Logical AND Operator for Opening Objects

Description

Combines two Opening objects with AND logic using the & operator. This creates an OpeningAll object.

Usage

## S4 method for signature 'Opening,Opening'
e1 & e2
## S4 method for signature 'Opening,Opening'
e1 & e2

Arguments

e1

(Opening) the first opening object.

e2

(Opening) the second opening object.

Value

An OpeningAll object combining e1 and e2.

Approximate posterior with (log) normal distribution

Description

To reproduce the resultant approximate model in the future exactly, include seed = xxxx in the call to approximate.

Usage

approximate(object, model, data, ...)

## S4 method for signature 'Samples'
approximate(
  object,
  model,
  data,
  points = seq(from = min(data@doseGrid), to = max(data@doseGrid), length = 5L),
  refDose = median(points),
  logNormal = FALSE,
  verbose = TRUE,
  create_plot = TRUE,
  ...
)
approximate(object, model, data, ...)

## S4 method for signature 'Samples'
approximate(
  object,
  model,
  data,
  points = seq(from = min(data@doseGrid), to = max(data@doseGrid), length = 5L),
  refDose = median(points),
  logNormal = FALSE,
  verbose = TRUE,
  create_plot = TRUE,
  ...
)

Arguments

object

the Samples object

model

the GeneralModel object

data

the Data object

...

additional arguments (see methods)

points

optional parameter, which gives the dose values at which the approximation should rely on (default: 5 values equally spaced from minimum to maximum of the dose grid)

refDose

the reference dose to be used (default: median of points)

logNormal

use the log-normal prior? (not default) otherwise, the normal prior for the logistic regression coefficients is used

verbose

be verbose (progress statements)? (default)

create_plot

add a ggplot2 object to the return value (default)

Value

a list containing the approximation model and, if requested, a ggplot2 object containing a graphical representation of the fitted model

Functions

approximate(Samples): Here the ... argument can transport additional arguments for Quantiles2LogisticNormal, e.g. in order to control the approximation quality, etc.

Examples

# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize a model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get posterior for all model parameters
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Approximate the posterior distribution with a bivariate normal
# max.time and maxit are very small only for the purpose of showing the example. They
# should be increased for a real case.
set.seed(94)
approximation <- approximate(
  object = samples,
  model = model,
  data = data,
  logNormal = TRUE,
  control = list(
    threshold.stop = 0.1,
    max.time = 1,
    maxit = 1
  )
)

posterior <- approximation$model

# nolint end
# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize a model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get posterior for all model parameters
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Approximate the posterior distribution with a bivariate normal
# max.time and maxit are very small only for the purpose of showing the example. They
# should be increased for a real case.
set.seed(94)
approximation <- approximate(
  object = samples,
  model = model,
  data = data,
  logNormal = TRUE,
  control = list(
    threshold.stop = 0.1,
    max.time = 1,
    maxit = 1
  )
)

posterior <- approximation$model

# nolint end

Additional Assertions for `checkmate`

Description

We provide additional assertion functions that can be used together with the checkmate functions. These are described in individual help pages linked below.

Value

Depending on the function prefix.

assert_ functions return the object invisibly if successful, and otherwise throw an error message.
check_ functions return TRUE if successful, otherwise a string with the error message.
test_ functions just return TRUE or FALSE.

`Backfill` class

Description

Class representing a backfilling rule in a clinical trial design.

Usage

Backfill(
  cohort_size = CohortSizeConst(size = 3),
  opening = OpeningMinDose(),
  recruitment = RecruitmentUnlimited(),
  max_size = 1000000L,
  priority = c("highest", "lowest", "random")
)

.DefaultBackfill()
Backfill(
  cohort_size = CohortSizeConst(size = 3),
  opening = OpeningMinDose(),
  recruitment = RecruitmentUnlimited(),
  max_size = 1000000L,
  priority = c("highest", "lowest", "random")
)

.DefaultBackfill()

Arguments

cohort_size

(CohortSize)
see the slot definition.

opening

(Opening)
see the slot definition.

recruitment

(Recruitment)
see the slot definition.

max_size

(count)
see the slot definition.

priority

(character)
see the slot definition.

Value

An object of class Backfill.

Slots

cohort_size: (CohortSize)
the size of cohorts to be backfilled.
opening: (Opening)
the opening criteria for backfilling.
recruitment: (Recruitment)
recruitment criteria for backfilling.
max_size: (count)
the maximum number of patients to be backfilled.
priority: (character)
the priority rule for backfilling, one of "highest", "lowest", or "random".

Note

Typically, end users will not use the .DefaultBackfill() function.

Get the Biomarker Levels for a Given Dual-Endpoint Model, Given Dose Levels and Samples

Description

Usage

biomarker(xLevel, model, samples, ...)

## S4 method for signature 'integer,DualEndpoint,Samples'
biomarker(xLevel, model, samples, ...)
biomarker(xLevel, model, samples, ...)

## S4 method for signature 'integer,DualEndpoint,Samples'
biomarker(xLevel, model, samples, ...)

Arguments

xLevel

(integer)
the levels for the doses the patients have been given w.r.t dose grid. See Data for more details.

model

(DualEndpoint)
the model.

samples

(Samples)
the samples of model's parameters that store the value of biomarker levels for all doses on the dose grid.

...

not used.

Details

This function simply returns a specific columns (with the indices equal to xLevel) of the biomarker samples matrix, which is included in the the samples object.

Value

The biomarker levels.

Functions

biomarker(xLevel = integer, model = DualEndpoint, samples = Samples):

Examples

# Create the data.
my_data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  ID = 1:17,
  cohort = c(
    1L,
    2L,
    3L,
    4L,
    5L,
    6L,
    6L,
    6L,
    7L,
    7L,
    7L,
    8L,
    8L,
    8L,
    9L,
    9L,
    9L
  ),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1).
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 500
)
my_samples <- mcmc(my_data, my_model, my_options)

# Obtain the biomarker levels (samples) for the second dose from the dose grid,
# which is 0.5.
biomarker(
  xLevel = 2L,
  model = my_model,
  samples = my_samples
)
# Create the data.
my_data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  ID = 1:17,
  cohort = c(
    1L,
    2L,
    3L,
    4L,
    5L,
    6L,
    6L,
    6L,
    7L,
    7L,
    7L,
    8L,
    8L,
    8L,
    9L,
    9L,
    9L
  ),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1).
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 500
)
my_samples <- mcmc(my_data, my_model, my_options)

# Obtain the biomarker levels (samples) for the second dose from the dose grid,
# which is 0.5.
biomarker(
  xLevel = 2L,
  model = my_model,
  samples = my_samples
)

Check if All Arguments Are Equal

Description

Elements of ... must be numeric vectors or scalars.

This function performs an element-by-element comparison of the first object provided in ... with every other object in ... and returns TRUE if all comparisons are equal within a given tolerance and FALSE otherwise.

Elements of ... must be numeric vectors or scalars.

This function performs an element-by-element comparison of the first object provided in ... with every other object in ... and throws an error if they are not.

Usage

check_equal(..., tol = sqrt(.Machine$double.eps))

assert_equal(
  ...,
  tol = sqrt(.Machine$double.eps),
  .var.name = vname(x),
  add = NULL
)
check_equal(..., tol = sqrt(.Machine$double.eps))

assert_equal(
  ...,
  tol = sqrt(.Machine$double.eps),
  .var.name = vname(x),
  add = NULL
)

Arguments

...

(numeric)
vectors to be compared

tol

(numeric)
the maximum difference to be tolerated when judging equality

.var.name

[character(1)]
Name of the checked object to print in assertions. Defaults to the heuristic implemented in vname.

add

[AssertCollection]
Collection to store assertion messages. See AssertCollection.

Value

TRUE if all element-by-element differences are less than tolerance in magnitude, FALSE otherwise.

list(...), invisibly.

Note

If there are any missing or infinite values in ..., this function returns FALSE, regardless of the values of other elements in ....

If elements in ... are not all of the same length, FALSE is returned.

If there are any missing or infinite values in ..., this function throws an error, regardless of the values of other elements in ....

If elements in ... are not all of the same length, an error is thrown.

Examples

check_equal(1:2, 1:2) # TRUE
check_equal(1:2, 2:3) # "Not all equal"
check_equal(Inf, Inf) # "Not all equal"
check_equal(0.01, 0.02) # "Not all equal"
check_equal(0.01, 0.02, tol = 0.05) # TRUE
check_equal(1, c(1, 1)) # "Not all equal"
assert_equal(1:2, 1:2) # no error
assert_equal(0.01, 0.02, tol = 0.05) # no error
check_equal(1:2, 1:2) # TRUE
check_equal(1:2, 2:3) # "Not all equal"
check_equal(Inf, Inf) # "Not all equal"
check_equal(0.01, 0.02) # "Not all equal"
check_equal(0.01, 0.02, tol = 0.05) # TRUE
check_equal(1, c(1, 1)) # "Not all equal"
assert_equal(1:2, 1:2) # no error
assert_equal(0.01, 0.02, tol = 0.05) # no error

Check that an argument is a valid format specification

Description

Usage

check_format(x, len = NULL, min.len = NULL, max.len = NULL)

assert_format(
  x,
  len = NULL,
  min.len = NULL,
  max.len = NULL,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_format(x, len = NULL, min.len = NULL, max.len = NULL)

expect_format(
  x,
  len = NULL,
  min.len = NULL,
  max.len = NULL,
  info = NULL,
  label = vname(x)
)
check_format(x, len = NULL, min.len = NULL, max.len = NULL)

assert_format(
  x,
  len = NULL,
  min.len = NULL,
  max.len = NULL,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_format(x, len = NULL, min.len = NULL, max.len = NULL)

expect_format(
  x,
  len = NULL,
  min.len = NULL,
  max.len = NULL,
  info = NULL,
  label = vname(x)
)

Arguments

x

[any]
Object to check.

len

[integer(1)]
Exact expected length of x.

min.len

[integer(1)]
Minimal length of x.

max.len

[integer(1)]
Maximal length of x.

.var.name

[character(1)]
Name of the checked object to print in assertions. Defaults to the heuristic implemented in vname.

add

[AssertCollection]
Collection to store assertion messages. See AssertCollection.

info

[character(1)]
Extra information to be included in the message for the testthat reporter. See expect_that.

label

[character(1)]
Name of the checked object to print in messages. Defaults to the heuristic implemented in vname.

Value

TRUE if successful, otherwise a string with the error message.

Examples

check_format("%5.2f")
check_format("%5.2f")

Check if vectors are of compatible lengths

Description

Two vectors are of compatible size if and only if:

At least one vector has size 1
or both vectors are of the same size.

Usage

check_length(x, len)

assert_length(x, len, .var.name = checkmate::vname(x), add = NULL)

test_length(x, len)
check_length(x, len)

assert_length(x, len, .var.name = checkmate::vname(x), add = NULL)

test_length(x, len)

Arguments

x

(any)
the first vector, any object for which length() function is defined.

len

(count)
the length of the second vector.

.var.name

[character(1)]
Name of the checked object to print in assertions. Defaults to the heuristic implemented in vname.

add

[AssertCollection]
Collection to store assertion messages. See AssertCollection.

Value

TRUE if successful, otherwise a string with the error message.

Examples

check_length(1:5, 1)
check_length(1:5, 6)
check_length(1:5, 5)
check_length(10, 1)
check_length(10, 9)
check_length(1:5, 1)
check_length(1:5, 6)
check_length(1:5, 5)
check_length(10, 1)
check_length(10, 9)

Check if an argument is a probability vector

Description

Check if every element in a given numerical vector or matrix represents a probability, that is a number within (0, 1) interval, that can optionally be closed at any side.

Usage

check_probabilities(
  x,
  bounds_closed = TRUE,
  len = NULL,
  unique = FALSE,
  sorted = FALSE
)

assert_probabilities(
  x,
  bounds_closed = TRUE,
  len = NULL,
  unique = FALSE,
  sorted = FALSE,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_probabilities(
  x,
  bounds_closed = TRUE,
  len = NULL,
  unique = FALSE,
  sorted = FALSE
)

expect_probabilities(
  x,
  bounds_closed = TRUE,
  len = NULL,
  unique = FALSE,
  sorted = FALSE,
  info = NULL,
  label = vname(x)
)
check_probabilities(
  x,
  bounds_closed = TRUE,
  len = NULL,
  unique = FALSE,
  sorted = FALSE
)

assert_probabilities(
  x,
  bounds_closed = TRUE,
  len = NULL,
  unique = FALSE,
  sorted = FALSE,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_probabilities(
  x,
  bounds_closed = TRUE,
  len = NULL,
  unique = FALSE,
  sorted = FALSE
)

expect_probabilities(
  x,
  bounds_closed = TRUE,
  len = NULL,
  unique = FALSE,
  sorted = FALSE,
  info = NULL,
  label = vname(x)
)

Arguments

x

(numeric)
vector or matrix with numerical values to check.

bounds_closed

(logical)
should bounds be closed? This can be a scalar or vector of length two. If it is a scalar, then its value applies equally to lower bound $0$ and upper bound $1$ . If this is a vector with two flags, the first flag corresponds to the lower bound $0$ only, and the second to the upper bound $1$ only.

len

[integer(1)]
Exact expected length of x.

unique

[logical(1)]
Must all values be unique? Default is FALSE.

sorted

[logical(1)]
Elements must be sorted in ascending order. Missing values are ignored.

.var.name

[character(1)]
Name of the checked object to print in assertions. Defaults to the heuristic implemented in vname.

add

[AssertCollection]
Collection to store assertion messages. See AssertCollection.

info

[character(1)]
Extra information to be included in the message for the testthat reporter. See expect_that.

label

[character(1)]
Name of the checked object to print in messages. Defaults to the heuristic implemented in vname.

Value

TRUE if successful, otherwise a string with the error message.

Note

If there are any missing or non-finite values in x, this function returns FALSE, regardless of the values of other elements in x.

Examples

x <- c(0, 0.2, 0.1, 0.3, 1)
check_probabilities(x)
check_probabilities(x, bounds_closed = FALSE)
check_probabilities(x, bounds_closed = c(FALSE, TRUE))
x <- c(0, 0.2, 0.1, 0.3, 1)
check_probabilities(x)
check_probabilities(x, bounds_closed = FALSE)
check_probabilities(x, bounds_closed = c(FALSE, TRUE))

Check if an argument is a single probability value

Description

Check if a given value represents a probability, that is a number within (0, 1) interval, that can optionally be closed at any side.

Usage

check_probability(x, bounds_closed = TRUE)

assert_probability(
  x,
  bounds_closed = TRUE,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_probability(x, bounds_closed = TRUE)

expect_probability(x, bounds_closed = TRUE, info = NULL, label = vname(x))
check_probability(x, bounds_closed = TRUE)

assert_probability(
  x,
  bounds_closed = TRUE,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_probability(x, bounds_closed = TRUE)

expect_probability(x, bounds_closed = TRUE, info = NULL, label = vname(x))

Arguments

x

(number)
a single value to check.

bounds_closed

.var.name

[character(1)]
Name of the checked object to print in assertions. Defaults to the heuristic implemented in vname.

add

[AssertCollection]
Collection to store assertion messages. See AssertCollection.

info

[character(1)]
Extra information to be included in the message for the testthat reporter. See expect_that.

label

[character(1)]
Name of the checked object to print in messages. Defaults to the heuristic implemented in vname.

Value

TRUE if successful, otherwise a string with the error message.

Examples

check_probability(0.5)
check_probability(0, bounds_closed = FALSE)
check_probability(0, bounds_closed = c(FALSE, TRUE))
check_probability(0.5)
check_probability(0, bounds_closed = FALSE)
check_probability(0, bounds_closed = c(FALSE, TRUE))

Check if an argument is a probability range

Description

Check if a given numerical interval represents a probability range, that is a sub-interval of (0, 1) interval, that can optionally be closed at any side.

Usage

check_probability_range(x, bounds_closed = TRUE)

assert_probability_range(
  x,
  bounds_closed = TRUE,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_probability_range(x, bounds_closed = TRUE)

expect_probability_range(
  x,
  bounds_closed = TRUE,
  info = NULL,
  label = vname(x)
)
check_probability_range(x, bounds_closed = TRUE)

assert_probability_range(
  x,
  bounds_closed = TRUE,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_probability_range(x, bounds_closed = TRUE)

expect_probability_range(
  x,
  bounds_closed = TRUE,
  info = NULL,
  label = vname(x)
)

Arguments

x

(number)
an interval to check.

bounds_closed

.var.name

[character(1)]
Name of the checked object to print in assertions. Defaults to the heuristic implemented in vname.

add

[AssertCollection]
Collection to store assertion messages. See AssertCollection.

info

[character(1)]
Extra information to be included in the message for the testthat reporter. See expect_that.

label

[character(1)]
Name of the checked object to print in messages. Defaults to the heuristic implemented in vname.

Value

TRUE if successful, otherwise a string with the error message.

Examples

x <- c(0, 0.2)
check_probability_range(x)
check_probability_range(rev(x))
check_probability_range(x, bounds_closed = FALSE)
check_probability_range(x, bounds_closed = c(FALSE, TRUE))
x <- c(0, 0.2)
check_probability_range(x)
check_probability_range(rev(x))
check_probability_range(x, bounds_closed = FALSE)
check_probability_range(x, bounds_closed = c(FALSE, TRUE))

Check that an argument is a numerical range

Description

An argument x is a numerical range if and only if (all conditions must be met):

Is an object of type: integer or double.
Is a vector or length two such that the value of the first number is not less than the second number. Equalness is allowed if and only if unique flag is set to TRUE.
Lower bound of the interval is greater than or equal to lower and upper bound of the interval is less than or equal to upper.
It contains only finite (given that finite is TRUE) and non-missing values.

Usage

check_range(x, lower = -Inf, upper = Inf, finite = FALSE, unique = TRUE)

assert_range(
  x,
  lower = -Inf,
  upper = Inf,
  finite = FALSE,
  unique = TRUE,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_range(x, lower = -Inf, upper = Inf, finite = FALSE, unique = TRUE)

expect_range(
  x,
  lower = -Inf,
  upper = Inf,
  finite = FALSE,
  unique = TRUE,
  info = NULL,
  label = vname(x)
)
check_range(x, lower = -Inf, upper = Inf, finite = FALSE, unique = TRUE)

assert_range(
  x,
  lower = -Inf,
  upper = Inf,
  finite = FALSE,
  unique = TRUE,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_range(x, lower = -Inf, upper = Inf, finite = FALSE, unique = TRUE)

expect_range(
  x,
  lower = -Inf,
  upper = Inf,
  finite = FALSE,
  unique = TRUE,
  info = NULL,
  label = vname(x)
)

Arguments

x

[any]
Object to check.

lower

[numeric(1)]
Lower value all elements of x must be greater than or equal to.

upper

[numeric(1)]
Upper value all elements of x must be lower than or equal to.

finite

[logical(1)]
Check for only finite values? Default is FALSE.

unique

[logical(1)]
Must all values be unique? Default is FALSE.

.var.name

[character(1)]
Name of the checked object to print in assertions. Defaults to the heuristic implemented in vname.

add

[AssertCollection]
Collection to store assertion messages. See AssertCollection.

info

[character(1)]
Extra information to be included in the message for the testthat reporter. See expect_that.

label

[character(1)]
Name of the checked object to print in messages. Defaults to the heuristic implemented in vname.

Value

TRUE if successful, otherwise a string with the error message.

Examples

check_range(c(1, 5))
check_range(c(-5, 1))
check_range(c(4, 1))
check_range(c(1, 1))
check_range(c(1, 1), unique = FALSE)
check_range(1:3)
check_range(c(1, 5))
check_range(c(-5, 1))
check_range(c(4, 1))
check_range(c(1, 1))
check_range(c(1, 1), unique = FALSE)
check_range(1:3)

`CohortSizeConst`

Description

CohortSizeConst is the class for fixed and constant size of cohort.

Usage

CohortSizeConst(size)

.DefaultCohortSizeConst()
CohortSizeConst(size)

.DefaultCohortSizeConst()

Arguments

size

(number)
see slot definition.

Slots

size: (integer)
cohort size.

Note

Typically, end users will not use the .DefaultCohortSizeConst() function.

Examples

# Cohort of size 3, constant along the study.
my_size <- CohortSizeConst(size = 3)
# Cohort of size 3, constant along the study.
my_size <- CohortSizeConst(size = 3)

`CohortSizeDLT`

Description

CohortSizeDLT is the class for cohort size based on number of DLTs.

Usage

CohortSizeDLT(intervals, cohort_size)

.DefaultCohortSizeDLT()
CohortSizeDLT(intervals, cohort_size)

.DefaultCohortSizeDLT()

Arguments

intervals

(numeric)
see slot definition.

cohort_size

(numeric)
see slot definition.

Slots

intervals: (integer)
a vector with the left bounds of the relevant DLT intervals.
cohort_size: (integer)
a vector with the cohort sizes corresponding to the elements of intervals.

Note

Typically, end users will not use the .DefaultCohortSizeDLT() function.

Examples

# Rule for having cohort of size 1 until no DLT is observed and having cohort
# of size 3 as soon as 1 DLT is observed.
my_size <- CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))
# Rule for having cohort of size 1 until no DLT is observed and having cohort
# of size 3 as soon as 1 DLT is observed.
my_size <- CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))

`CohortSizeMax`

Description

CohortSizeMax is the class for cohort size that is based on maximum of multiple cohort size rules. The cohort_sizes slot stores a set of cohort size rules, which are again the objects of class CohortSize. The maximum of these individual cohort sizes is taken to give the final cohort size.

Usage

.DefaultCohortSizeMax()

CohortSizeMax(cohort_sizes)
.DefaultCohortSizeMax()

CohortSizeMax(cohort_sizes)

Arguments

cohort_sizes

(list)
see slot definition.

Slots

cohort_sizes: (list)
a list of cohort size rules, i.e. objects of class CohortSize.

Note

Typically, end users will not use the .DefaultCohortSizeMax() function.

Examples

# Rule for cohort of size 1 for doses <30 and cohort of size 3 for doses >=30.
my_size1 <- CohortSizeRange(intervals = c(0, 10), cohort_size = c(1, 3))

# Rule for cohort of size 1 until no DLT were observed and cohort of size 3
# as soon as 1 DLT is observed.
my_size2 <- CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))

# Cohort size rules of class 'CohortSizeMax' which will then be combined with
# the 'max' operation.
mySize <- CohortSizeMax(cohort_sizes = list(my_size1, my_size2))
# Rule for cohort of size 1 for doses <30 and cohort of size 3 for doses >=30.
my_size1 <- CohortSizeRange(intervals = c(0, 10), cohort_size = c(1, 3))

# Rule for cohort of size 1 until no DLT were observed and cohort of size 3
# as soon as 1 DLT is observed.
my_size2 <- CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))

# Cohort size rules of class 'CohortSizeMax' which will then be combined with
# the 'max' operation.
mySize <- CohortSizeMax(cohort_sizes = list(my_size1, my_size2))

`CohortSizeMin`

Description

CohortSizeMin is the class for cohort size that is based on minimum of multiple cohort size rules. The cohort_sizes slot stores a set of cohort size rules, which are again the objects of class CohortSize. The minimum of these individual cohort sizes is taken to give the final cohort size.

Usage

CohortSizeMin(cohort_sizes)

.DefaultCohortSizeMin()
CohortSizeMin(cohort_sizes)

.DefaultCohortSizeMin()

Arguments

cohort_sizes

(list)
see slot definition.

Slots

cohort_sizes: (list)
a list of cohort size rules, i.e. objects of class CohortSize.

Note

Typically, end users will not use the .DefaultCohortSizeMin() function.

Examples

# Rule for cohort of size 1 for doses <30 and cohort of size 3 for doses >=30.
my_size1 <- CohortSizeRange(intervals = c(0, 10), cohort_size = c(1, 3))

# Rule for cohort of size 1 until no DLT were observed and cohort of size 3
# as soon as 1 DLT is observed.
my_size2 <- CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))

# Cohort size rules of class 'CohortSizeMin' which will then be combined with
# the 'min' operation.
my_size <- CohortSizeMin(cohort_sizes = list(my_size1, my_size2))
# Rule for cohort of size 1 for doses <30 and cohort of size 3 for doses >=30.
my_size1 <- CohortSizeRange(intervals = c(0, 10), cohort_size = c(1, 3))

# Rule for cohort of size 1 until no DLT were observed and cohort of size 3
# as soon as 1 DLT is observed.
my_size2 <- CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))

# Cohort size rules of class 'CohortSizeMin' which will then be combined with
# the 'min' operation.
my_size <- CohortSizeMin(cohort_sizes = list(my_size1, my_size2))

`CohortSizeOrdinal`

Description

CohortSizeOrdinal is the class for cohort size for an ordinal CRM trial.

Usage

CohortSizeOrdinal(grade, rule)

.DefaultCohortSizeOrdinal()
CohortSizeOrdinal(grade, rule)

.DefaultCohortSizeOrdinal()

Arguments

grade

(integer)
see slot definition.

rule

(CohortSize)
see slot definition.

Slots

grade: (integer)
the grade at which the rule should be applied
rule: (CohortSize)
the CohortSize rule to apply.

Note

Typically, end users will not use the .DefaultCohortSizeOrdinal() function.

Examples

CohortSizeOrdinal(
  grade = 1L,
  rule = CohortSizeRange(intervals = c(0, 30), cohort_size = c(1L, 3L))
)
CohortSizeOrdinal(
  grade = 1L,
  rule = CohortSizeRange(intervals = c(0, 30), cohort_size = c(1L, 3L))
)

`CohortSizeParts`

Description

CohortSizeParts is the class for cohort size that changes for the second part of the dose escalation. It works only in conjunction with DataParts objects.

Usage

CohortSizeParts(cohort_sizes)

.DefaultCohortSizeParts()
CohortSizeParts(cohort_sizes)

.DefaultCohortSizeParts()

Arguments

cohort_sizes

(numeric)
see slot definition.

Slots

cohort_sizes: (integer)
a vector of length two with two sizes, one for part 1, and one for part 2 respectively.

Note

Typically, end users will not use the .DefaultCohortSizeParts() function.

Examples

# Part 1 cohort size = 1, Part 2 cohort size = 3.
my_size <- CohortSizeParts(cohort_sizes = c(1, 3))
# Part 1 cohort size = 1, Part 2 cohort size = 3.
my_size <- CohortSizeParts(cohort_sizes = c(1, 3))

`CohortSizeRandom`

Description

CohortSizeRandom is the class for random cohort sizes drawn from a uniform distribution between min_size and max_size (inclusive).

Usage

CohortSizeRandom(min_size, max_size)

.DefaultCohortSizeRandom()
CohortSizeRandom(min_size, max_size)

.DefaultCohortSizeRandom()

Arguments

min_size

(integer)
see slot definition.

max_size

(integer)
see slot definition.

Slots

min_size: (integer)
minimum cohort size.
max_size: (integer)
maximum cohort size.

Note

Typically, end users will not use the .DefaultCohortSizeRandom() function.

Examples

# Random cohort size between 1 and 5.
my_size <- CohortSizeRandom(min_size = 1, max_size = 5)
# Random cohort size between 1 and 5.
my_size <- CohortSizeRandom(min_size = 1, max_size = 5)

`CohortSizeRange`

Description

CohortSizeRange is the class for cohort size based on dose range.

Usage

CohortSizeRange(intervals, cohort_size)

.DefaultCohortSizeRange()
CohortSizeRange(intervals, cohort_size)

.DefaultCohortSizeRange()

Arguments

intervals

(numeric)
see slot definition.

cohort_size

(numeric)
see slot definition.

Slots

intervals: (numeric)
a vector with the left bounds of the relevant dose intervals.
cohort_size: (integer)
an integer vector with the cohort sizes corresponding to the elements of intervals.

Note

Typically, end users will not use the .DefaultCohortSizeRange() function.

Examples

# Example for the rule having cohort of size 1 for doses <30
# and having cohort of size 3 for doses >=30.

my_size <- CohortSizeRange(intervals = c(0, 30), cohort_size = c(1, 3))
# Example for the rule having cohort of size 1 for doses <30
# and having cohort of size 3 for doses >=30.

my_size <- CohortSizeRange(intervals = c(0, 30), cohort_size = c(1, 3))

Object-oriented implementation of CRM designs

Description

Object-oriented implementation of CRM designs

Author(s)

Maintainer: Daniel Sabanés Bové [email protected] (ORCID)

Authors:

Wai Yin Yeung [email protected]
Burak Kuersad Guenhan [email protected]
Giuseppe Palermo [email protected]
Thomas Jaki [email protected]
Jiawen Zhu [email protected]
Ziwei Liao [email protected]
Dimitris Kontos [email protected]
Marlene Schulte-Goebel [email protected]
Doug Kelkhoff [email protected] (ORCID)
Oliver Boix [email protected]
Robert Adams [email protected]
Clara Beck [email protected]
John Kirkpatrick [email protected]
Wojciech Wójciak [email protected]
Guanya Peng
Prerana Chandratre

Other contributors:

F. Hoffmann-La Roche AG [copyright holder, funder]
Merck Healthcare KGaA [copyright holder, funder]
Bayer AG [copyright holder, funder]
RPACT GmbH [copyright holder, funder]

References

Sabanés Bové D, Yeung WY, Palermo G, Jaki T (2019). “Model-Based Dose Escalation Designs in R with crmPack.” Journal of Statistical Software, 89(10), 1–22. doi:10.18637/jss.v089.i10.

`CrmPackClass`

Description

CrmPackClass is a virtual class, from which all other crmPack classes inherit.

Open the Example PDF for crmPack

Description

Calling this helper function opens the example.pdf document, residing in the doc subfolder of the package installation directory.

Usage

crmPackExample()
crmPackExample()

Value

Called for side effects.

Open the Browser with Help Pages for crmPack

Description

This convenience function opens your browser with the help pages for crmPack.

Usage

crmPackHelp()
crmPackHelp()

Value

Called for side effects.

`DADesign`

Description

This class has special requirements for the model and data slots in comparison to the parent class Design:

Usage

DADesign(model, data, safetyWindow, ...)

.DefaultDADesign()
DADesign(model, data, safetyWindow, ...)

.DefaultDADesign()

Arguments

model

(GeneralModel)
see slot definition.

data

(DataDA)
see slot definition.

safetyWindow

(SafetyWindow)
see slot definition.

...

Arguments passed on to Design

stopping: (Stopping)
see slot definition.
increments: (Increments)
see slot definition.
pl_cohort_size: (CohortSize)
see slot definition.
backfill: (Backfill)
see slot definition.

Details

The safetyWindow slot should be an instance of the SafetyWindow class. It can be customized to specify the duration of the safety window for your trial. The safety window represents the time period required to observe toxicity data from the ongoing cohort before opening the next cohort. Note that even after opening the next cohort, further toxicity data will be collected and analyzed to make dose escalation decisions.

To specify a constant safety window, use the SafetyWindowConst constructor. For example:

mysafetywindow <- SafetyWindowConst(c(6, 2), 10, 20)

Slots

model: (GeneralModel)
the model to use, see in particular DALogisticLogNormal and TITELogisticLogNormal which make use of the time-to-DLT data.
data: (DataDA)
what is the dose grid, any previous data, etc.
safetyWindow: (SafetyWindow)
the safety window to apply between cohorts.

Note

Typically, end users will not use the .DefaultDADesign() function.

Examples

empty_data <- DataDA(
  doseGrid = c(
    0.1,
    0.5,
    1,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  Tmax = 60
)

npiece <- 10
t_max <- 60

lambda_prior <- function(k) {
  npiece / (t_max * (npiece - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece,
  l = as.numeric(t(apply(as.matrix(c(1:npiece), 1, npiece), 2, lambda_prior))),
  c_par = 2
)

my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)

my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

my_size <- maxSize(my_size1, my_size2)

my_stopping1 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)

my_stopping2 <- StoppingMinPatients(nPatients = 50)

my_stopping <- (my_stopping1 | my_stopping2) | StoppingMissingDose()

my_safety_window <- SafetyWindowConst(c(6, 2), 7, 7)

design <- DADesign(
  model = model,
  increments = my_increments,
  nextBest = my_next_best,
  stopping = my_stopping,
  cohort_size = my_size,
  data = empty_data,
  safetyWindow = my_safety_window,
  startingDose = 3
)
empty_data <- DataDA(
  doseGrid = c(
    0.1,
    0.5,
    1,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  Tmax = 60
)

npiece <- 10
t_max <- 60

lambda_prior <- function(k) {
  npiece / (t_max * (npiece - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece,
  l = as.numeric(t(apply(as.matrix(c(1:npiece), 1, npiece), 2, lambda_prior))),
  c_par = 2
)

my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)

my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

my_size <- maxSize(my_size1, my_size2)

my_stopping1 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)

my_stopping2 <- StoppingMinPatients(nPatients = 50)

my_stopping <- (my_stopping1 | my_stopping2) | StoppingMissingDose()

my_safety_window <- SafetyWindowConst(c(6, 2), 7, 7)

design <- DADesign(
  model = model,
  increments = my_increments,
  nextBest = my_next_best,
  stopping = my_stopping,
  cohort_size = my_size,
  data = empty_data,
  safetyWindow = my_safety_window,
  startingDose = 3
)

`DALogisticLogNormal`

Description

DALogisticLogNormal is the class for the logistic model with bivariate (log) normal prior and data augmentation. This class inherits from the LogisticLogNormal class.

Usage

DALogisticLogNormal(npiece = 3, l, c_par = 2, cond_pem = TRUE, ...)

.DefaultDALogisticLogNormal()
DALogisticLogNormal(npiece = 3, l, c_par = 2, cond_pem = TRUE, ...)

.DefaultDALogisticLogNormal()

Arguments

npiece

(number)
the number of pieces in the PEM.

l

(numeric)
a vector used in the lambda prior.

c_par

(numeric)
a parameter used in the lambda prior; according to Liu's paper, c_par = 2 is recommended.

cond_pem

(flag)
is a conditional piecewise-exponential model used? (default). Otherwise an unconditional model is used.

...

Arguments passed on to LogisticLogNormal

mean: (numeric)
the prior mean vector.
cov: (matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.
ref_dose: (number)
the reference dose $x*$ (strictly positive number).

Slots

npiece: (number)
the number of pieces in the PEM.
l: (numeric)
a vector used in the lambda prior.
c_par: (numeric)
a parameter used in the lambda prior; according to Liu's paper, c_par = 2 is recommended.
cond_pem: (flag)
is a conditional piecewise-exponential model used? (default). Otherwise an unconditional model is used.

Note

We still need to include here formula for the lambda prior.

Typically, end users will not use the .DefaultDALogisticLogNormal() function.

Examples

npiece <- 10
Tmax <- 60 # nolintr

lambda_prior <- function(k) {
  npiece / (Tmax * (npiece - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece,
  l = as.numeric(t(apply(as.matrix(c(1:npiece), 1, npiece), 2, lambda_prior))),
  c_par = 2
)
npiece <- 10
Tmax <- 60 # nolintr

lambda_prior <- function(k) {
  npiece / (Tmax * (npiece - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece,
  l = as.numeric(t(apply(as.matrix(c(1:npiece), 1, npiece), 2, lambda_prior))),
  c_par = 2
)

Apply a Function to Subsets of Data Frame.

Description

dapply splits the data df into the subsets defined by f, and applies function FUN to each of the subset. All the results are row-binded and returned as data.frame object.

Usage

dapply(df, f, FUN, ...)
dapply(df, f, FUN, ...)

Arguments

df

(⁠data frame⁠)
data set to be divided into groups.

f

(factor or formula or list)
a factor in the sense that as.factor(f) defines the grouping, or a list of such factors in which case their interaction is used for the grouping. f can also be a formula of the form ~ g1 + ... + gk to split by the interaction of the variables ⁠g1, ..., gk⁠. This parameter is passed directly into split() function.

FUN

(function)
the function to be applied to each subset of df defined by f.

...

parameters passed to lapply(), which is used when applying a function FUN over groups defined by f.

Value

The data.frame object with results from FUN.

Examples

df <- data.frame(
  dose = c(0.1, 6, 6, 5, 0.1, 5, 6, 6),
  cohort = c("B", "B", "B", "A", "A", "A", "B", "B")
)

dapply(
  df,
  f = ~cohort,
  FUN = function(coh) {
    data.frame(my_cohort = coh$cohort[1], my_max = max(coh$dose))
  }
)

dapply(
  df,
  f = ~cohort,
  FUN = function(coh) {
    coh$dose <- sort(coh$dose, decreasing = TRUE)
    coh
  }
)
df <- data.frame(
  dose = c(0.1, 6, 6, 5, 0.1, 5, 6, 6),
  cohort = c("B", "B", "B", "A", "A", "A", "B", "B")
)

dapply(
  df,
  f = ~cohort,
  FUN = function(coh) {
    data.frame(my_cohort = coh$cohort[1], my_max = max(coh$dose))
  }
)

dapply(
  df,
  f = ~cohort,
  FUN = function(coh) {
    coh$dose <- sort(coh$dose, decreasing = TRUE)
    coh
  }
)

`DASimulations`

Description

This class captures the trial simulations from DA based designs. In comparison to the parent class Simulations, it contains additional slots to capture the time to DLT fits, additional parameters and the trial duration.

Usage

DASimulations(trial_duration, ...)

.DefaultDASimulations()
DASimulations(trial_duration, ...)

.DefaultDASimulations()

Arguments

trial_duration

(numeric)
see DASimulations

...

additional parameters from Simulations

Slots

trial_duration: (numeric)
the vector of trial duration values for all simulations.

Note

Typically, end users will not use the .DefaultDASimulations() function.

`Data`

Description

Data is a class for the data input. It inherits from GeneralData.

Usage

Data(
  x = numeric(),
  y = integer(),
  ID = integer(),
  cohort = integer(),
  doseGrid = numeric(),
  placebo = FALSE,
  backfilled = rep(FALSE, length(x)),
  response = rep(NA_integer_, length(x)),
  ...
)

.DefaultData()
Data(
  x = numeric(),
  y = integer(),
  ID = integer(),
  cohort = integer(),
  doseGrid = numeric(),
  placebo = FALSE,
  backfilled = rep(FALSE, length(x)),
  response = rep(NA_integer_, length(x)),
  ...
)

.DefaultData()

Arguments

x

(numeric)
the doses for the patients.

y

(integer)
the vector of toxicity events (0 or 1). You can also supply numeric vectors, but these will then be converted to integer internally.

ID

(integer)
unique patient IDs. You can also supply numeric vectors, but these will then be converted to integer internally.

cohort

(integer)
the cohort (non-negative sorted) indices. You can also supply numeric vectors, but these will then be converted to integer internally.

doseGrid

(numeric)
all possible doses.

placebo

(flag)
if TRUE the first dose level in the doseGrid is considered as placebo.

backfilled

(logical)
whether each patient was in a backfill cohort.

response

(integer)
whether each patient had a positive efficacy response (1 = yes, 0 = no). May contain NA.

...

not used.

Details

The cohort can be missing if and only if placebo is equal to FALSE.

Slots

x: (numeric)
the doses for the patients.
y: (integer)
the vector of toxicity events (0 or 1 integers).
doseGrid: (numeric)
the vector of all possible doses (sorted), i.e. the dose grid.
nGrid: (integer)
number of gridpoints.
xLevel: (integer)
the levels for the doses the patients have been given, w.r.t doseGrid.
placebo: (logical)
if TRUE the first dose level in the doseGridis considered as PLACEBO.
backfilled: (logical)
whether this patient was in a backfill cohort.
response: (integer)
whether this patient had a positive efficacy response (0 or 1 integers).

Note

ID and cohort can be missing. Then a message will be issued and the variables will be filled with default IDs and best guesses cohort, i.e. a sorted (in ascending order) sequence of values from ⁠{1, 2, ...}⁠.

Typically, end users will not use the .DefaultData() function.

Examples

my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = as.integer(1:8),
  cohort = as.integer(c(1, 2, 3, 4, 5, 6, 6, 6)),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)
my_data
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = as.integer(1:8),
  cohort = as.integer(c(1, 2, 3, 4, 5, 6, 6, 6)),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)
my_data

`DataDA`

Description

DataDA is a class for the time-to-DLT augmented data. It inherits from Data and it contains additional DLT free survival times.

Usage

DataDA(
  u = numeric(),
  t0 = numeric(length(u)),
  Tmax = 0 + .Machine$double.xmin,
  ...
)

.DefaultDataDA()
DataDA(
  u = numeric(),
  t0 = numeric(length(u)),
  Tmax = 0 + .Machine$double.xmin,
  ...
)

.DefaultDataDA()

Arguments

u

(numeric)
the continuous vector of DLT free survival times.

t0

(numeric)
time of initial dosing for each patient. Non-negative values sorted in ascending order. Default to vector of 0s of length equal to length of u.

Tmax

(number)
the DLT observation period.

...

parameters passed to Data().

Slots

u: (numeric)
the continuous vector of DLT free survival times.
t0: (numeric)
time of initial dosing for each patient. Non-negative values sorted in ascending order.
Tmax: (number)
the DLT observation period.

Note

⁠survival time⁠ here refers to the time period for which the subject did not experience any DLT, and is not referring to deaths.

Typically, end users will not use the .DefaultDataDA() function.

Examples

my_data <- DataDA(
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60,
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Set up an empty data set.
empty_data <- DataDA(
  doseGrid = c(0.1, 0.5, 1, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  Tmax = 60
)
empty_data
my_data <- DataDA(
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60,
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Set up an empty data set.
empty_data <- DataDA(
  doseGrid = c(0.1, 0.5, 1, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  Tmax = 60
)
empty_data

`DataDual`

Description

DataDual is a class for the dual endpoint data. It inherits from Data and it contains additional biomarker information.

Usage

DataDual(w = numeric(), ...)

.DefaultDataDual()
DataDual(w = numeric(), ...)

.DefaultDataDual()

Arguments

w

(numeric)
the continuous vector of biomarker values.

...

parameters passed to Data().

Slots

w: (numeric)
the continuous vector of biomarker values.

Note

Typically, end users will not use the .DefaultDataDual() function.

Examples

my_data <- DataDual(
  w = rnorm(8),
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)
my_data
my_data <- DataDual(
  w = rnorm(8),
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)
my_data

`DataGrouped`

Description

DataGrouped is a class for a two groups dose escalation data set, comprised of a monotherapy (mono) and a combination therapy (combo) arm. It inherits from Data and it contains the additional group information.

Usage

DataGrouped(group = character(), ...)

.DefaultDataGrouped()
DataGrouped(group = character(), ...)

.DefaultDataGrouped()

Arguments

group

(factor or character)
whether mono or combo was used. If character then will be coerced to factor with the correct levels internally.

...

parameters passed to Data().

Slots

group: (factor)
whether mono or combo was used.

Note

Typically, end users will not use the .DefaultDataGrouped() function.

Examples

my_data <- DataGrouped(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  group = c("mono", "mono", "mono", "mono", "mono", "mono", "combo", "combo")
)

# Set up an empty data set.
empty_data <- DataGrouped(
  doseGrid = c(0.1, 0.5, 1, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
empty_data
my_data <- DataGrouped(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  group = c("mono", "mono", "mono", "mono", "mono", "mono", "combo", "combo")
)

# Set up an empty data set.
empty_data <- DataGrouped(
  doseGrid = c(0.1, 0.5, 1, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
empty_data

`DataMixture`

Description

DataMixture is a class for the data with mixture sharing. It inherits from Data and it contains additional information on the mixture sharing.

Usage

DataMixture(xshare = numeric(), yshare = integer(), ...)

.DefaultDataMixture()
DataMixture(xshare = numeric(), yshare = integer(), ...)

.DefaultDataMixture()

Arguments

xshare

(numeric)
the doses for the share patients.

yshare

(integer)
the vector of toxicity events (0 or 1) for the share patients. You can also supply numeric vectors, but these will then be converted to integer internally.

...

parameters passed to Data().

Slots

xshare: (numeric)
the doses for the share patients.
yshare: (integer)
the vector of toxicity events (0 or 1) for the share patients.
nObsshare: (count)
number of share patients.

Note

Typically, end users will not use the .DefaultDataMixture() function.

Examples

my_data <- DataMixture(
  xshare = c(12, 14, 16, 18.0),
  yshare = c(0L, 1L, 1L, 1L),
  nObsshare = 4L,
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  ID = 1:3,
  cohort = 1:3,
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
my_data
my_data <- DataMixture(
  xshare = c(12, 14, 16, 18.0),
  yshare = c(0L, 1L, 1L, 1L),
  nObsshare = 4L,
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  ID = 1:3,
  cohort = 1:3,
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
my_data

`DataOrdinal`

Description

DataOrdinal is a class for ordinal toxicity data. It inherits from GeneralData and it describes toxicity responses on an ordinal rather than binary scale.

Usage

DataOrdinal(
  x = numeric(),
  y = integer(),
  ID = integer(),
  cohort = integer(),
  doseGrid = numeric(),
  placebo = FALSE,
  yCategories = c(`No DLT` = 0L, DLT = 1L),
  ...
)

.DefaultDataOrdinal()
DataOrdinal(
  x = numeric(),
  y = integer(),
  ID = integer(),
  cohort = integer(),
  doseGrid = numeric(),
  placebo = FALSE,
  yCategories = c(`No DLT` = 0L, DLT = 1L),
  ...
)

.DefaultDataOrdinal()

Arguments

x

(numeric)
the doses for the patients.

y

(integer)
the vector of toxicity events (0 or 1). You can also supply numeric vectors, but these will then be converted to integer internally.

ID

(integer)
unique patient IDs. You can also supply numeric vectors, but these will then be converted to integer internally.

cohort

(integer)
the cohort (non-negative sorted) indices. You can also supply numeric vectors, but these will then be converted to integer internally.

doseGrid

(numeric)
all possible doses.

placebo

(flag)
if TRUE the first dose level in the doseGrid is considered as placebo.

yCategories

(named integer)
the names and codes for the toxicity categories used in the data. Category labels are taken from the names of the vector. The names of the vector must be unique and its values must be sorted and take the values 0, 1, 2, ...

...

not used.

Details

The cohort can be missing if and only if placebo is equal to FALSE.

Note

This class has been implemented as a sibling of the existing Data class (rather than as a parent or child) to minimise the risk of unintended side effects on existing classes and methods.

The default setting for the yCategories slot replicates the behaviour of the existing Data class.

Typically, end users will not use the .DefaultDataOrdinal() function.

Examples

DataOrdinal(
  x = c(10, 20, 30, 40, 50, 50, 50, 60, 60, 60),
  y = as.integer(c(0, 0, 0, 0, 0, 1, 0, 0, 1, 2)),
  ID = 1L:10L,
  cohort = as.integer(c(1:4, 5, 5, 5, 6, 6, 6)),
  doseGrid = c(seq(from = 10, to = 100, by = 10)),
  yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L),
  placebo = FALSE
)
DataOrdinal(
  x = c(10, 20, 30, 40, 50, 50, 50, 60, 60, 60),
  y = as.integer(c(0, 0, 0, 0, 0, 1, 0, 0, 1, 2)),
  ID = 1L:10L,
  cohort = as.integer(c(1:4, 5, 5, 5, 6, 6, 6)),
  doseGrid = c(seq(from = 10, to = 100, by = 10)),
  yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L),
  placebo = FALSE
)

`DataParts`

Description

DataParts is a class for the data with two study parts. It inherits from Data and it contains additional information on the two study parts.

Usage

DataParts(part = integer(), nextPart = 1L, part1Ladder = numeric(), ...)

.DefaultDataParts()
DataParts(part = integer(), nextPart = 1L, part1Ladder = numeric(), ...)

.DefaultDataParts()

Arguments

part

(integer)
which part does each of the patients belong to?

nextPart

(count)
what is the part for the next cohort (1 or 2)?

part1Ladder

(numeric)
what is the escalation ladder for part 1? This shall be an ordered subset of the doseGrid.

...

parameters passed to Data().

Slots

part: (integer)
which part does each of the patients belong to?
nextPart: (count)
what is the part for the next cohort (1 or 2)?
part1Ladder: (numeric)
what is the escalation ladder for part 1? This shall be an ordered subset of the doseGrid.

Note

Typically, end users will not use the .DefaultDataParts() function.

Examples

my_data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  ID = 1:3,
  cohort = 1:3,
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)
my_data
my_data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  ID = 1:3,
  cohort = 1:3,
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)
my_data

`Design`

Description

Design is the class for rule-based designs. The difference between this class and its parent RuleDesign class is that Design class contains additional model, stopping and increments slots.

Usage

Design(
  model,
  stopping,
  increments,
  pl_cohort_size = CohortSizeConst(0L),
  backfill = Backfill(opening = OpeningNone()),
  ...
)

.DefaultDesign()
Design(
  model,
  stopping,
  increments,
  pl_cohort_size = CohortSizeConst(0L),
  backfill = Backfill(opening = OpeningNone()),
  ...
)

.DefaultDesign()

Arguments

model

(GeneralModel)
see slot definition.

stopping

(Stopping)
see slot definition.

increments

(Increments)
see slot definition.

pl_cohort_size

(CohortSize)
see slot definition.

backfill

(Backfill)
see slot definition.

...

Arguments passed on to RuleDesign

nextBest: (NextBest)
see slot definition.
cohort_size: (CohortSize)
see slot definition.
data: (Data)
see slot definition.
startingDose: (number)
see slot definition.

Slots

model: (GeneralModel)
the model to be used.
stopping: (Stopping)
stopping rule(s) for the trial.
increments: (Increments)
how to control increments between dose levels.
pl_cohort_size: (CohortSize)
rules for the cohort sizes for placebo, if any planned (defaults to constant 0 placebo patients).
backfill: (Backfill)
rules for backfilling patients in the trial (defaults to no backfilling).

Note

Typically, end users will not use the .DefaultDesign() function.

Examples

empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- (my_stopping1 & my_stopping2) |
  my_stopping3 |
  StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
design <- Design(
  model = my_model,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = empty_data,
  startingDose = 3
)
empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- (my_stopping1 & my_stopping2) |
  my_stopping3 |
  StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
design <- Design(
  model = my_model,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = empty_data,
  startingDose = 3
)

`DesignGrouped`

Description

DesignGrouped combines two Design objects: one for the mono and one for the combo arm of a joint dose escalation design.

Usage

DesignGrouped(
  model,
  mono,
  combo = mono,
  first_cohort_mono_only = TRUE,
  same_dose_for_all = !same_dose_for_start,
  same_dose_for_start = FALSE,
  stop_mono_with_combo = FALSE,
  ...
)
DesignGrouped(
  model,
  mono,
  combo = mono,
  first_cohort_mono_only = TRUE,
  same_dose_for_all = !same_dose_for_start,
  same_dose_for_start = FALSE,
  stop_mono_with_combo = FALSE,
  ...
)

Arguments

model

(LogisticLogNormalGrouped)
see slot definition.

mono

(Design)
see slot definition.

combo

(Design)
see slot definition.

first_cohort_mono_only

(flag)
see slot definition.

same_dose_for_all

(flag)
see slot definition.

same_dose_for_start

(flag)
see slot definition.

stop_mono_with_combo

(flag)
whether the mono arm should be stopped when the combo arm is stopped (this makes sense when the only real trial objective is the recommended combo dose).

...

not used.

Details

Note that the model slots inside the mono and combo parameters are ignored (because we don't fit separate regression models for the mono and combo arms). Instead, the model parameter is used to fit a joint regression model for the mono and combo arms together.
same_dose_for_start = TRUE is useful as an option when we want to use same_dose_for_all = FALSE combined with first_cohort_mono_only = TRUE. This will allow to randomize patients to the mono and combo arms at the same dose as long as the selected dose for the cohorts stay the same. This can therefore further mitigate bias as long as possible between the mono and combo arms.

Slots

model: (LogisticLogNormalGrouped)
the model to be used, currently only one class is allowed.
mono: (Design)
defines the dose escalation rules for the mono arm, see details.
combo: (Design)
defines the dose escalation rules for the combo arm, see details.
first_cohort_mono_only: (flag)
whether first test one mono agent cohort, and then once its DLT data has been collected, we proceed from the second cohort onwards with concurrent mono and combo cohorts.
same_dose_for_all: (flag)
whether the lower dose of the separately determined mono and combo doses should be used as the next dose for both mono and combo in all cohorts.
same_dose_for_start: (flag)
indicates whether, when mono and combo are used in the same cohort for the first time, the same dose should be used for both. Note that this is different from same_dose_for_all which will always force them to be the same. If same_dose_for_all = TRUE, this is therefore ignored. See Details.

Note

Typically, end-users will not use the .DefaultDesignGrouped() function.

Examples

empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the joint model.
my_model <- LogisticLogNormalGrouped(
  mean = c(-0.85, 0, 1, 0),
  cov = diag(1, 4),
  ref_dose = 56
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- (my_stopping1 & my_stopping2) |
  my_stopping3 |
  StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Rules to be used for both arms.
one_arm <- Design(
  model = .DefaultModelLogNormal(), # Ignored.
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = empty_data,
  startingDose = 3
)

# Initialize the design.
design <- DesignGrouped(
  model = my_model,
  mono = one_arm
)

# Alternative options: Here e.g.
# - use both mono in first cohort and afterwards have mono and combo in parallel,
# - in general allow different dose levels for the cohorts,
# - but for the start (i.e. second cohort) have the same dose for mono and combo.
# - Stop mono arm too, when combo arm is stopped.

design2 <- DesignGrouped(
  model = my_model,
  mono = one_arm,
  first_cohort_mono_only = TRUE,
  same_dose_for_all = FALSE,
  same_dose_for_start = TRUE,
  stop_mono_with_combo = TRUE
)
empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the joint model.
my_model <- LogisticLogNormalGrouped(
  mean = c(-0.85, 0, 1, 0),
  cov = diag(1, 4),
  ref_dose = 56
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- (my_stopping1 & my_stopping2) |
  my_stopping3 |
  StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Rules to be used for both arms.
one_arm <- Design(
  model = .DefaultModelLogNormal(), # Ignored.
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = empty_data,
  startingDose = 3
)

# Initialize the design.
design <- DesignGrouped(
  model = my_model,
  mono = one_arm
)

# Alternative options: Here e.g.
# - use both mono in first cohort and afterwards have mono and combo in parallel,
# - in general allow different dose levels for the cohorts,
# - but for the start (i.e. second cohort) have the same dose for mono and combo.
# - Stop mono arm too, when combo arm is stopped.

design2 <- DesignGrouped(
  model = my_model,
  mono = one_arm,
  first_cohort_mono_only = TRUE,
  same_dose_for_all = FALSE,
  same_dose_for_start = TRUE,
  stop_mono_with_combo = TRUE
)

`DesignOrdinal`

Description

DesignOrdinal is the class for rule-based ordinal designs. The difference between this class and its parent RuleDesignOrdinal class is that the DesignOrdinal class contains additional model, stopping, increments and pl_cohort_size slots.

Usage

DesignOrdinal(
  model,
  stopping,
  increments,
  pl_cohort_size = CohortSizeOrdinal(1L, CohortSizeConst(0L)),
  ...
)

.DefaultDesignOrdinal()
DesignOrdinal(
  model,
  stopping,
  increments,
  pl_cohort_size = CohortSizeOrdinal(1L, CohortSizeConst(0L)),
  ...
)

.DefaultDesignOrdinal()

Arguments

model

(LogisticLogNormalOrdinal)
see slot definition.

stopping

(Stopping)
see slot definition.

increments

(Increments)
see slot definition.

pl_cohort_size

(CohortSize)
see slot definition.

...

Arguments passed on to RuleDesignOrdinal

next_best: (NextBestOrdinal)
see slot definition.
cohort_size: (CohortSize)
see slot definition.
data: (DataOrdinal)
see slot definition.
starting_dose: (number)
see slot definition.

Details

Please note that stopping, increments or cohort size rules need to be wrapped into the corresponding StoppingOrdinal, IncrementsOrdinal or CohortSizeOrdinal classes, before a successful evaluation of the corresponding methods can take place. Note also that these wrappers cannot be nested, i.e., you cannot have an IncrementsOrdinal inside another IncrementsOrdinal (which also would not make sense) because it would not be clear which event grade to use for the methods calculation. However, multiple rules can be combined using the operators defined for these classes, e.g., StoppingOrdinal(1L, rule1 & rule2) | StoppingOrdinal(2L, rule3).

Slots

model: (LogisticLogNormalOrdinal)
the model to be used.
stopping: (Stopping)
stopping rule(s) for the trial.
increments: (Increments)
how to control increments between dose levels.
pl_cohort_size: (CohortSize)
rules for the cohort sizes for placebo, if any planned (defaults to constant 0 placebo patients).

Note

Typically, end users will not use the .DefaultDesignOrdinal() function.

Examples

my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- CohortSizeOrdinal(1L, maxSize(my_size1, my_size2))

my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- StoppingOrdinal(
  1L,
  (my_stopping1 & my_stopping2) | my_stopping3 | StoppingMissingDose()
)

# Initialize the design.
design <- DesignOrdinal(
  model = LogisticLogNormalOrdinal(
    mean = c(-3, -4, 1),
    cov = diag(c(3, 4, 1)),
    ref_dose = 50
  ),
  next_best = NextBestOrdinal(
    1L,
    NextBestNCRM(
      target = c(0.2, 0.35),
      overdose = c(0.35, 1),
      max_overdose_prob = 0.25
    )
  ),
  stopping = my_stopping,
  increments = IncrementsOrdinal(
    1L,
    IncrementsRelative(
      intervals = c(0, 20),
      increments = c(1, 0.33)
    )
  ),
  cohort_size = my_size,
  data = DataOrdinal(
    doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100),
    yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L)
  ),
  starting_dose = 3
)
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- CohortSizeOrdinal(1L, maxSize(my_size1, my_size2))

my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- StoppingOrdinal(
  1L,
  (my_stopping1 & my_stopping2) | my_stopping3 | StoppingMissingDose()
)

# Initialize the design.
design <- DesignOrdinal(
  model = LogisticLogNormalOrdinal(
    mean = c(-3, -4, 1),
    cov = diag(c(3, 4, 1)),
    ref_dose = 50
  ),
  next_best = NextBestOrdinal(
    1L,
    NextBestNCRM(
      target = c(0.2, 0.35),
      overdose = c(0.35, 1),
      max_overdose_prob = 0.25
    )
  ),
  stopping = my_stopping,
  increments = IncrementsOrdinal(
    1L,
    IncrementsRelative(
      intervals = c(0, 20),
      increments = c(1, 0.33)
    )
  ),
  cohort_size = my_size,
  data = DataOrdinal(
    doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100),
    yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L)
  ),
  starting_dose = 3
)

Computing the Doses for a given independent variable, Model and Samples

Description

A function that computes the dose reaching a specific target value of a given variable that dose depends on. The meaning of this variable depends on the type of the model. For instance, for single agent dose escalation model or pseudo DLE (dose-limiting events)/toxicity model, this variable represents the a probability of the occurrence of a DLE. For efficacy models, it represents expected efficacy. The doses are computed based on the samples of the model parameters (samples).

Usage

dose(x, model, samples, ...)

## S4 method for signature 'numeric,LogisticNormal,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticLogNormal,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticLogNormalOrdinal,Samples'
dose(x, model, samples, grade)

## S4 method for signature 'numeric,LogisticLogNormalSub,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,ProbitLogNormal,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,ProbitLogNormalRel,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticLogNormalGrouped,Samples'
dose(x, model, samples, group)

## S4 method for signature 'numeric,LogisticKadane,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticKadaneBetaGamma,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticNormalMixture,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticNormalFixedMixture,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticLogNormalMixture,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,DualEndpoint,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticIndepBeta,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticIndepBeta,missing'
dose(x, model)

## S4 method for signature 'numeric,Effloglog,missing'
dose(x, model)

## S4 method for signature 'numeric,EffFlexi,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,OneParLogNormalPrior,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,OneParExpPrior,Samples'
dose(x, model, samples)
dose(x, model, samples, ...)

## S4 method for signature 'numeric,LogisticNormal,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticLogNormal,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticLogNormalOrdinal,Samples'
dose(x, model, samples, grade)

## S4 method for signature 'numeric,LogisticLogNormalSub,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,ProbitLogNormal,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,ProbitLogNormalRel,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticLogNormalGrouped,Samples'
dose(x, model, samples, group)

## S4 method for signature 'numeric,LogisticKadane,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticKadaneBetaGamma,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticNormalMixture,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticNormalFixedMixture,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticLogNormalMixture,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,DualEndpoint,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticIndepBeta,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,LogisticIndepBeta,missing'
dose(x, model)

## S4 method for signature 'numeric,Effloglog,missing'
dose(x, model)

## S4 method for signature 'numeric,EffFlexi,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,OneParLogNormalPrior,Samples'
dose(x, model, samples)

## S4 method for signature 'numeric,OneParExpPrior,Samples'
dose(x, model, samples)

Arguments

x

(proportion or numeric)
a value of an independent variable on which dose depends. The following recycling rule applies when samples is not missing: vectors of size 1 will be recycled to the size of the sample (i.e. size(samples)). Otherwise, x must have the same size as the sample.

model

(GeneralModel or ModelPseudo)
the model.

samples

(Samples)
the samples of model's parameters that will be used to compute the resulting doses. Can also be missing for some models.

...

model specific parameters when samples are not used.

grade

(integer)
The toxicity grade for which probabilities are required

group

(character or factor)
for LogisticLogNormalGrouped, indicating whether to calculate the dose for the mono or for the combo arm.

Details

The dose() function computes the doses corresponding to a value of a given independent variable, using samples of the model parameter(s). If you work with multivariate model parameters, then assume that your model specific dose() method receives a samples matrix where the rows correspond to the sampling index, i.e. the layout is then ⁠nSamples x dimParameter⁠.

Value

A number or numeric vector with the doses. If non-scalar samples were used, then every element in the returned vector corresponds to one element of a sample. Hence, in this case, the output vector is of the same length as the sample vector. If scalar samples were used or no samples were used, e.g. for pseudo DLE/toxicity model, then the output is of the same length as the length of the prob.

Functions

dose(x = numeric, model = LogisticNormal, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = LogisticLogNormal, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = LogisticLogNormalOrdinal, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).

In the case of a LogisticLogNormalOrdinal model, dose returns only the probability of toxicity at the given grade or higher
dose(x = numeric, model = LogisticLogNormalSub, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = ProbitLogNormal, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = ProbitLogNormalRel, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = LogisticLogNormalGrouped, samples = Samples): method for LogisticLogNormalGrouped which needs group argument in addition.
dose(x = numeric, model = LogisticKadane, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = LogisticKadaneBetaGamma, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = LogisticNormalMixture, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = LogisticNormalFixedMixture, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = LogisticLogNormalMixture, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = DualEndpoint, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = LogisticIndepBeta, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x).
dose(x = numeric, model = LogisticIndepBeta, samples = missing): compute the dose level reaching a specific target probability of the occurrence of a DLE (x). All model parameters (except x) should be present in the model object.
dose(x = numeric, model = Effloglog, samples = missing): compute the dose level reaching a specific target probability of the occurrence of a DLE (x). All model parameters (except x) should be present in the model object.
dose(x = numeric, model = EffFlexi, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLE (x). For this method x must be a scalar.
dose(x = numeric, model = OneParLogNormalPrior, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLT (x).
dose(x = numeric, model = OneParExpPrior, samples = Samples): compute the dose level reaching a specific target probability of the occurrence of a DLT (x).

Note

The dose() and prob() methods are the inverse of each other, for all dose() methods for which its first argument, i.e. a given independent variable that dose depends on, represents toxicity probability.

Examples

# Create some data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model, e.g. 'LogisticLogNormal'.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get samples from posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 20)
my_samples <- mcmc(data = my_data, model = my_model, options = my_options)

# Posterior for the dose achieving Prob(DLT) = 0.45.
dose(x = 0.45, model = my_model, samples = my_samples)

# Create data from the 'Data' (or 'DataDual') class.
dlt_data <- Data(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

# Initialize a toxicity model using 'LogisticIndepBeta' model.
dlt_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = dlt_data
)

# Get samples from posterior.
dlt_sample <- mcmc(data = dlt_data, model = dlt_model, options = my_options)

# Posterior for the dose achieving Prob(DLT) = 0.45.
dose(x = 0.45, model = dlt_model, samples = dlt_sample)
dose(x = c(0.45, 0.6), model = dlt_model)
data_ordinal <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(data_ordinal, model, options)

dose(0.25, model, samples, grade = 2L)
# Create some data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model, e.g. 'LogisticLogNormal'.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get samples from posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 20)
my_samples <- mcmc(data = my_data, model = my_model, options = my_options)

# Posterior for the dose achieving Prob(DLT) = 0.45.
dose(x = 0.45, model = my_model, samples = my_samples)

# Create data from the 'Data' (or 'DataDual') class.
dlt_data <- Data(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

# Initialize a toxicity model using 'LogisticIndepBeta' model.
dlt_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = dlt_data
)

# Get samples from posterior.
dlt_sample <- mcmc(data = dlt_data, model = dlt_model, options = my_options)

# Posterior for the dose achieving Prob(DLT) = 0.45.
dose(x = 0.45, model = dlt_model, samples = dlt_sample)
dose(x = c(0.45, 0.6), model = dlt_model)
data_ordinal <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(data_ordinal, model, options)

dose(0.25, model, samples, grade = 2L)

Getting the Dose Grid Range

Description

A function that returns a vector of length two with the minimum and maximum dose in a grid. It returns c(-Inf, Inf) if the range cannot be determined, which happens when the dose grid is empty. User can choose whether the placebo dose (if any) should be counted or not.

Usage

dose_grid_range(object, ...)

## S4 method for signature 'Data'
dose_grid_range(object, ignore_placebo = TRUE)

## S4 method for signature 'DataOrdinal'
dose_grid_range(object, ignore_placebo = TRUE)
dose_grid_range(object, ...)

## S4 method for signature 'Data'
dose_grid_range(object, ignore_placebo = TRUE)

## S4 method for signature 'DataOrdinal'
dose_grid_range(object, ignore_placebo = TRUE)

Arguments

object

(Data)
object with dose grid.

...

further arguments passed to class-specific methods.

ignore_placebo

(flag)
should placebo dose (if any) not be counted?

Value

A numeric vector containing the minimum and maximum of all the doses in a grid or c(-Inf, Inf).

Examples

my_data <- Data(
  x = c(10, 50, 90, 100, 0.001, 20, 30, 30),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(1L, 2L, 3L, 4L, 5L, 5L, 6L, 6L),
  doseGrid = c(0.001, seq(from = 10, to = 100, by = 10)),
  placebo = TRUE
)
dose_grid_range(my_data)
dose_grid_range(my_data, ignore_placebo = FALSE)
data <- DataOrdinal(
  x = c(10, 20, 30, 40, 50, 50, 50, 60, 60, 60),
  y = as.integer(c(0, 0, 0, 0, 0, 1, 0, 0, 1, 2)),
  ID = 1L:10L,
  cohort = as.integer(c(1:4, 5, 5, 5, 6, 6, 6)),
  doseGrid = c(seq(from = 10, to = 100, by = 10)),
  yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L),
  placebo = FALSE
)

dose_grid_range(data)
my_data <- Data(
  x = c(10, 50, 90, 100, 0.001, 20, 30, 30),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(1L, 2L, 3L, 4L, 5L, 5L, 6L, 6L),
  doseGrid = c(0.001, seq(from = 10, to = 100, by = 10)),
  placebo = TRUE
)
dose_grid_range(my_data)
dose_grid_range(my_data, ignore_placebo = FALSE)
data <- DataOrdinal(
  x = c(10, 20, 30, 40, 50, 50, 50, 60, 60, 60),
  y = as.integer(c(0, 0, 0, 0, 0, 1, 0, 0, 1, 2)),
  ID = 1L:10L,
  cohort = as.integer(c(1:4, 5, 5, 5, 6, 6, 6)),
  doseGrid = c(seq(from = 10, to = 100, by = 10)),
  yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L),
  placebo = FALSE
)

dose_grid_range(data)

Getting the Dose Function for a Given Model Type

Description

A function that returns a dose() method that computes the dose reaching a specific target value of a given independent variable, based on the model specific parameters.

Usage

doseFunction(model, ...)

## S4 method for signature 'GeneralModel'
doseFunction(model, ...)

## S4 method for signature 'ModelPseudo'
doseFunction(model, ...)

## S4 method for signature 'LogisticLogNormalOrdinal'
doseFunction(model, grade, ...)
doseFunction(model, ...)

## S4 method for signature 'GeneralModel'
doseFunction(model, ...)

## S4 method for signature 'ModelPseudo'
doseFunction(model, ...)

## S4 method for signature 'LogisticLogNormalOrdinal'
doseFunction(model, grade, ...)

Arguments

model

(GeneralModel or ModelPseudo)
the model.

...

model specific parameters.

grade

(integer)
the toxicity grade for which the dose function is required

Value

A dose() method that computes doses.

Functions

doseFunction(GeneralModel):
doseFunction(ModelPseudo):
doseFunction(LogisticLogNormalOrdinal):

Examples

my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

dose_fun <- doseFunction(my_model, alpha0 = 2, alpha1 = 3)
dose_fun(0.6)
data_ordinal <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
suppressWarnings({
  samples <- mcmc(data_ordinal, model, options)
})

doseFunction(
  model,
  alpha1 = samples@data$alpha2,
  beta = samples@data$beta,
  grade = 1L
)(x = 0.75)
doseFunction(
  model,
  alpha2 = samples@data$alpha2,
  beta = samples@data$beta,
  grade = 2L
)(x = 0.25)
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

dose_fun <- doseFunction(my_model, alpha0 = 2, alpha1 = 3)
dose_fun(0.6)
data_ordinal <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()
suppressWarnings({
  samples <- mcmc(data_ordinal, model, options)
})

doseFunction(
  model,
  alpha1 = samples@data$alpha2,
  beta = samples@data$beta,
  grade = 1L
)(x = 0.75)
doseFunction(
  model,
  alpha2 = samples@data$alpha2,
  beta = samples@data$beta,
  grade = 2L
)(x = 0.25)

`DualDesign`

Description

DualDesign is the class for the dual-endpoint CRM design. This class has special requirements for the model and data slots in comparison to the parent class Design.

Usage

DualDesign(model, data, ...)

.DefaultDualDesign()
DualDesign(model, data, ...)

.DefaultDualDesign()

Arguments

model

(DualEndpoint)
see slot definition.

data

(DataDual)
see slot definition.

...

Arguments passed on to Design

stopping: (Stopping)
see slot definition.
increments: (Increments)
see slot definition.
pl_cohort_size: (CohortSize)
see slot definition.
backfill: (Backfill)
see slot definition.

Slots

model: (DualEndpoint)
the model to be used.
data: (DataDual)
specifies dose grid, any previous data, etc.

Note

the nextBest slot can be of any class, this allows for easy comparison with recommendation methods that don't use the biomarker information.

Typically, end users will not use the .DefaultDualDesign() function.

Examples

empty_data <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
my_stopping <- my_stopping1 | StoppingMinPatients(40) | StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
design <- DualDesign(
  model = my_model,
  data = empty_data,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  startingDose = 3
)
empty_data <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
my_stopping <- my_stopping1 | StoppingMinPatients(40) | StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
design <- DualDesign(
  model = my_model,
  data = empty_data,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  startingDose = 3
)

`DualEndpoint`

Description

DualEndpoint is the general class for the dual endpoint model.

Usage

DualEndpoint(mean, cov, ref_dose = 1, use_log_dose = FALSE, sigma2W, rho)

.DefaultDualEndpoint()
DualEndpoint(mean, cov, ref_dose = 1, use_log_dose = FALSE, sigma2W, rho)

.DefaultDualEndpoint()

Arguments

mean

(numeric)
for the probit toxicity model, the prior mean vector.

cov

(matrix)
for the probit toxicity model, the prior covariance matrix. The precision matrix is internally calculated as an inverse of cov.

ref_dose

(number)
for the probit toxicity model, the reference dose $x*$ (strictly positive number).

use_log_dose

(flag)
for the probit toxicity model, whether a log transformation of the (standardized) dose should be used?

sigma2W

(numeric)
the biomarker variance. Either a fixed value or Inverse-Gamma distribution parameters, i.e. vector with two elements named a and b.

rho

(numeric)
either a fixed value for the correlation (between -1 and 1), or a named vector with two elements named a and b for the Beta prior on the transformation kappa = (rho + 1) / 2, which is in ⁠(0, 1)⁠. For example, ⁠a = 1, b = 1⁠ leads to a uniform prior on rho.

Details

The idea of the dual-endpoint models is to model not only the dose-toxicity relationship, but also to model, at the same time, the relationship of a PD biomarker with the dose. The sub-classes of this class define how the dose-biomarker relationship is parametrized. This class here shall contain all the common features to reduce duplicate code. (This class however, must not be virtual as we need to create objects of it during the construction of subclass objects.)

The dose-toxicity relationship is modeled with probit regression model

$probit[p(x)] = betaZ1 + betaZ2 * x/x*,$

$probit[p(x)] = betaZ1 + betaZ2 * log(x/x*),$

in case when the option use_log_dose is TRUE. Here, $p(x)$ is the probability of observing a DLT for a given dose $x$ and $x*$ is the reference dose. The prior

$(betaZ1, log(betaZ2)) ~ Normal(mean, cov).$

For the biomarker response $w$ at a dose $x$ , we assume

$w(x) ~ Normal(f(x), sigma2W),$

where $f(x)$ is a function of the dose $x$ , which is further specified in sub-classes. The biomarker variance $sigma2W$ can be fixed or assigned an Inverse-Gamma prior distribution; see the details below under slot sigma2W.

Finally, the two endpoints $y$ (the binary DLT variable) and $w$ (the biomarker) can be correlated, by assuming a correlation of level $rho$ between the underlying continuous latent toxicity variable $z$ and the biomarker $w$ . Again, this correlation can be fixed or assigned a prior distribution from the scaled Beta family; see the details below under slot rho.

Please see the example vignette by typing crmPackExample() for a full example.

Slots

betaZ_params: (ModelParamsNormal)
for the probit toxicity model, it contains the prior mean, covariance matrix and precision matrix which is internally calculated as an inverse of the covariance matrix.
ref_dose: (positive_number)
for the probit toxicity model, the reference dose.
use_log_dose: (flag)
for the probit toxicity model, whether a log transformation of the (standardized) dose should be used?
sigma2W: (numeric)
the biomarker variance. Either a fixed value or Inverse-Gamma distribution parameters, i.e. vector with two elements named a and b.
rho: (numeric)
either a fixed value for the correlation (between -1 and 1), or a named vector with two elements named a and b for the Beta prior on the transformation kappa = (rho + 1) / 2, which is in ⁠(0, 1)⁠. For example, ⁠a = 1, b = 1⁠ leads to a uniform prior on rho.
use_fixed: (logical)
indicates whether a fixed value for sigma2W or rho (for each parameter separately) is used or not. This slot is needed for internal purposes and must not be touched by the user.

Note

Typically, end users will not use the .DefaultDualEndpoint() function.

`DualEndpointBeta`

Description

DualEndpointBeta is the class for the dual endpoint model with beta function for dose-biomarker relationship.

Usage

DualEndpointBeta(E0, Emax, delta1, mode, ref_dose_beta = 1, ...)

.DefaultDualEndpointBeta()
DualEndpointBeta(E0, Emax, delta1, mode, ref_dose_beta = 1, ...)

.DefaultDualEndpointBeta()

Arguments

E0

(numeric)
either a fixed number or the two uniform distribution parameters.

Emax

(numeric)
either a fixed number or the two uniform distribution parameters.

delta1

(numeric)
either a fixed positive number or the two parameters of the uniform distribution, that can take only positive values.

mode

(numeric)
either a fixed positive number or the two parameters of the uniform distribution, that can take only positive values.

ref_dose_beta

(number)
the reference dose $x*$ (strictly positive number). Note that this is different from the ref_dose in the inherited DualEndpoint model).

...

parameters passed to DualEndpoint().

Details

This class extends the DualEndpoint class so that the dose-biomarker relationship $f(x)$ is modelled by a parametric, rescaled beta density function:

$f(x) = E0 + (Emax - E0) * Beta(delta1, delta2) * (x/x*)^{delta1} * (1 - x/x*)^{delta2},$

where $x*$ is the maximum dose (end of the dose range to be considered), $delta1$ and $delta2$ are the two beta function parameters, and $E0$ , $Emax$ are the minimum and maximum levels, respectively. For ease of interpretation, we use the parametrization based on $delta1$ and the mode, where

$mode = delta1 / (delta1 + delta2),$

so that multiplying this by $x*$ gives the mode on the dose grid.

All parameters can currently be assigned uniform distributions or be fixed in advance. Note that E0 and Emax can have negative values or uniform distributions reaching into negative range, while delta1 and mode must be positive or have uniform distributions in the positive range.

Slots

E0: (numeric)
either a fixed number or the two uniform distribution parameters.
Emax: (numeric)
either a fixed number or the two uniform distribution parameters.
delta1: (numeric)
either a fixed positive number or the two parameters of the uniform distribution, that can take only positive values.
mode: (numeric)
either a fixed positive number or the two parameters of the uniform distribution, that can take only positive values.
ref_dose_beta: (positive_number)
the reference dose $x*$ (note that this is different from the ref_dose in the inherited DualEndpoint model).

Note

Typically, end users will not use the .DefaultDualEndpointBeta() function.

Examples

my_model <- DualEndpointBeta(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  ref_dose = 10,
  use_log_dose = TRUE,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  E0 = c(0, 100),
  Emax = c(0, 500),
  delta1 = c(0, 5),
  mode = c(1, 15),
  ref_dose_beta = 1000
)
my_model <- DualEndpointBeta(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  ref_dose = 10,
  use_log_dose = TRUE,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  E0 = c(0, 100),
  Emax = c(0, 500),
  delta1 = c(0, 5),
  mode = c(1, 15),
  ref_dose_beta = 1000
)

`DualEndpointEmax`

Description

DualEndpointEmax is the class for the dual endpoint model with Emax function for dose-biomarker relationship.

Usage

DualEndpointEmax(E0, Emax, ED50, ref_dose_emax = 1, ...)

.DefaultDualEndpointEmax()
DualEndpointEmax(E0, Emax, ED50, ref_dose_emax = 1, ...)

.DefaultDualEndpointEmax()

Arguments

E0

(numeric)
either a fixed number or the two uniform distribution parameters.

Emax

(numeric)
either a fixed number or the two uniform distribution parameters.

ED50

(numeric)
either a fixed number or the two uniform distribution parameters.

ref_dose_emax

(number)
the reference dose $x*$ (strictly positive number). Note that this is different from the ref_dose in the inherited DualEndpoint model).

...

parameters passed to DualEndpoint().

Details

This class extends the DualEndpoint class so that the dose-biomarker relationship $f(x)$ is modelled by a parametric Emax function:

$f(x) = E0 + [(Emax - E0) * (x/x*)]/[ED50 + (x/x*)],$

where $x*$ is a reference dose, $E0$ and $Emax$ are the minimum and maximum levels for the biomarker, and $ED50$ is the dose achieving half of the maximum effect $0.5 * Emax$ . All parameters can currently be assigned uniform distributions or be fixed.

Slots

E0: (numeric)
either a fixed number or the two uniform distribution parameters.
Emax: (numeric)
either a fixed number or the two uniform distribution parameters.
ED50: (numeric)
either a fixed number or the two uniform distribution parameters.
ref_dose_emax: (positive_number)
the reference dose $x*$ (note that this is different from the ref_dose in the inherited DualEndpoint model).

Note

Typically, end users will not use the .DefaultDualEndpointEmax() function.

Examples

my_model <- DualEndpointEmax(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  E0 = c(0, 100),
  Emax = c(0, 500),
  ED50 = c(10, 200),
  ref_dose_emax = 1000
)
my_model <- DualEndpointEmax(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  E0 = c(0, 100),
  Emax = c(0, 500),
  ED50 = c(10, 200),
  ref_dose_emax = 1000
)

`DualEndpointRW`

Description

DualEndpointRW is the class for the dual endpoint model with random walk prior for biomarker.

Usage

DualEndpointRW(sigma2betaW, rw1 = TRUE, ...)

.DefaultDualEndpointRW()
DualEndpointRW(sigma2betaW, rw1 = TRUE, ...)

.DefaultDualEndpointRW()

Arguments

sigma2betaW

(numeric)
the prior variance factor of the random walk prior for the biomarker model. Either a fixed value or Inverse-Gamma distribution parameters, i.e. vector with two elements named a and b.

rw1

(flag)
for specifying the random walk prior on the biomarker level. When TRUE, random walk of first order is used. Otherwise, the random walk of second order is used.

...

parameters passed to DualEndpoint().

Details

This class extends the DualEndpoint class so that the dose-biomarker relationship $f(x)$ is modelled by a non-parametric random walk of first or second order. That means, for the first order random walk we assume

$betaW_i - betaW_i-1 ~ Normal(0, (x_i - x_i-1) * sigma2betaW),$

where $betaW_i = f(x_i)$ is the biomarker mean at the $i$ -th dose gridpoint $x_i$ . For the second order random walk, the second-order differences instead of the first-order differences of the biomarker means follow the normal distribution with $0$ mean and $2 * (x_i - x_i-2) * sigma2betaW$ variance.

The variance parameter $sigma2betaW$ is important because it steers the smoothness of the function $f(x)$ , i.e.: if it is large, then $f(x)$ will be very wiggly; if it is small, then $f(x)$ will be smooth. This parameter can either be a fixed value or assigned an inverse gamma prior distribution.

Slots

sigma2betaW: (numeric)
the prior variance factor of the random walk prior for the biomarker model. Either a fixed value or Inverse-Gamma distribution parameters, i.e. vector with two elements named a and b.
rw1: (flag)
for specifying the random walk prior on the biomarker level. When TRUE, random walk of first order is used. Otherwise, the random walk of second order is used.

Note

Non-equidistant dose grids can be used now, because the difference $x_i - x_i-1$ is included in the modelling assumption above. Please note that due to impropriety of the random walk prior distributions, it is not possible to produce MCMC samples with empty data objects (i.e., sample from the prior). This is not a bug, but a theoretical feature of this model.

Typically, end users will not use the .DefaultDualEndpointRW() function.

Examples

my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  sigma2betaW = 0.01,
  rw1 = TRUE
)
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  sigma2betaW = 0.01,
  rw1 = TRUE
)

`DualResponsesDesign.R`

Description

This is a class of design based on DLE responses using the LogisticIndepBeta model without DLE and efficacy samples. It contains all slots from the RuleDesign and TDsamplesDesign classes.

Usage

DualResponsesDesign(eff_model, data, ...)

.DefaultDualResponsesDesign()
DualResponsesDesign(eff_model, data, ...)

.DefaultDualResponsesDesign()

Arguments

eff_model

(ModelEff)
see slot definition.

data

(DataDual)
see slot definition.

...

Arguments passed on to TDDesign

model: (ModelTox)
see slot definition.
stopping: (Stopping)
see slot definition.
increments: (Increments)
see slot definition.
pl_cohort_size: (CohortSize)
see slot definition.

Slots

data: (DataDual)
the data set.
eff_model: (ModelEff)
the pseudo efficacy model to be used.

Note

Typically, end users will not use the .DefaultDualResponsesDesign() function.

Examples

empty_data <- DataDual(doseGrid = seq(25, 300, 25))

tox_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)

eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = empty_data
)

my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

my_increments <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)

my_size <- CohortSizeConst(size = 3)
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

design <- DualResponsesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = tox_model,
  eff_model = eff_model,
  data = empty_data,
  stopping = my_stopping,
  increments = my_increments
)
empty_data <- DataDual(doseGrid = seq(25, 300, 25))

tox_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)

eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = empty_data
)

my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

my_increments <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)

my_size <- CohortSizeConst(size = 3)
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

design <- DualResponsesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = tox_model,
  eff_model = eff_model,
  data = empty_data,
  stopping = my_stopping,
  increments = my_increments
)

`DualResponsesSamplesDesign`

Description

This is a class of design based on DLE responses using the LogisticIndepBeta model with DLE and efficacy samples. It contain all slots in RuleDesign and TDsamplesDesign class objects.

Usage

DualResponsesSamplesDesign(eff_model, data, ...)

.DefaultDualResponsesSamplesDesign()
DualResponsesSamplesDesign(eff_model, data, ...)

.DefaultDualResponsesSamplesDesign()

Arguments

eff_model

(ModelEff)
see slot definition.

data

(DataDual)
see slot definition.

...

Arguments passed on to TDsamplesDesign

model: (ModelTox)
see slot definition.
stopping: (Stopping)
see slot definition.
increments: (Increments)
see slot definition.
pl_cohort_size: (CohortSize)
see slot definition.

Slots

data: (DataDual)
the data set.
eff_model: (ModelEff)
the pseudo efficacy model to be used.

Note

Typically, end users will not use the .DefaultDualResponsesSamplesDesign() function.

Examples

empty_data <- DataDual(doseGrid = seq(25, 300, 25))

tox_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
tox_samples <- mcmc(empty_data, tox_model, options)

eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = empty_data
)
eff_samples <- mcmc(empty_data, eff_model, options)

my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

my_increments <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)
my_size <- CohortSizeConst(size = 3)
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = tox_model,
  eff_model = eff_model,
  data = empty_data,
  stopping = my_stopping,
  increments = my_increments
)
empty_data <- DataDual(doseGrid = seq(25, 300, 25))

tox_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
tox_samples <- mcmc(empty_data, tox_model, options)

eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = empty_data
)
eff_samples <- mcmc(empty_data, eff_model, options)

my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

my_increments <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)
my_size <- CohortSizeConst(size = 3)
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = tox_model,
  eff_model = eff_model,
  data = empty_data,
  stopping = my_stopping,
  increments = my_increments
)

`DualSimulations`

Description

This class captures the trial simulations from dual-endpoint model based designs. In comparison to the parent class Simulations, it contains additional slots to capture the dose-biomarker fits, and the sigma2W and rho estimates.

Usage

DualSimulations(rho_est, sigma2w_est, fit_biomarker, ...)

.DefaultDualSimulations()
DualSimulations(rho_est, sigma2w_est, fit_biomarker, ...)

.DefaultDualSimulations()

Arguments

rho_est

(numeric)
see DualSimulations

sigma2w_est

(numeric)
DualSimulations

fit_biomarker

(list)
see DualSimulations

...

additional parameters from Simulations

Slots

rho_est: (numeric)
vector of final posterior median rho estimates
sigma2w_est: (numeric)
vector of final posterior median sigma2W estimates
fit_biomarker: (list)
with the final dose-biomarker curve fits

Note

Typically, end users will not use the .DefaultDualSimulations() function.

Examples

data_list <- list(
  Data(
    x = 1:2,
    y = 0:1,
    doseGrid = 1:2,
    ID = 1L:2L,
    cohort = 1L:2L
  ),
  Data(
    x = 3:4,
    y = 0:1,
    doseGrid = 3:4,
    ID = 1L:2L,
    cohort = 1L:2L
  )
)

doses <- c(1, 2)
seed <- as.integer(123)

fit <- list(
  c(0.1, 0.2),
  c(0.3, 0.4)
)

stop_report <- matrix(c(TRUE, FALSE), nrow = 2)

stop_reasons <- list("A", "B")

additional_stats <- list(a = 1, b = 1)

dual_simulations_obj <- DualSimulations(
  rho_est = c(0.25, 0.35),
  sigma2w_est = c(0.15, 0.25),
  fit_biomarker = list(c(0.3, 0.4), c(0.4, 0.5)),
  fit = fit,
  stop_report = stop_report,
  stop_reasons = stop_reasons,
  additional_stats = additional_stats,
  data = data_list,
  doses = doses,
  seed = seed
)
data_list <- list(
  Data(
    x = 1:2,
    y = 0:1,
    doseGrid = 1:2,
    ID = 1L:2L,
    cohort = 1L:2L
  ),
  Data(
    x = 3:4,
    y = 0:1,
    doseGrid = 3:4,
    ID = 1L:2L,
    cohort = 1L:2L
  )
)

doses <- c(1, 2)
seed <- as.integer(123)

fit <- list(
  c(0.1, 0.2),
  c(0.3, 0.4)
)

stop_report <- matrix(c(TRUE, FALSE), nrow = 2)

stop_reasons <- list("A", "B")

additional_stats <- list(a = 1, b = 1)

dual_simulations_obj <- DualSimulations(
  rho_est = c(0.25, 0.35),
  sigma2w_est = c(0.15, 0.25),
  fit_biomarker = list(c(0.3, 0.4), c(0.4, 0.5)),
  fit = fit,
  stop_report = stop_report,
  stop_reasons = stop_reasons,
  additional_stats = additional_stats,
  data = data_list,
  doses = doses,
  seed = seed
)

`DualSimulationsSummary`

Description

This class captures the summary of dual-endpoint simulations output. In comparison to its parent class SimulationsSummary, it has additional slots.

Usage

.DefaultDualSimulationsSummary()
.DefaultDualSimulationsSummary()

Slots

biomarker_fit_at_dose_most_selected: (numeric)
fitted biomarker level at most often selected dose.
mean_biomarker_fit: (list)
list with average, lower (2.5%) and upper (97.5%) quantiles of mean fitted biomarker level at each dose

Note

Typically, end users will not use the .DefaultDualSimulationsSummary() function.

`EffFlexi`

Description

EffFlexi is the class for the efficacy model in flexible form of prior expressed in form of pseudo data. In this class, a flexible form is used to describe the relationship between the efficacy responses and the dose levels and it is specified as

$(W | betaW, sigma2W) ~ Normal(X * betaW, sigma2W * I),$

where $W$ is a vector of the efficacy responses, $betaW$ is a column vector of the mean efficacy responses for all dose levels, and $X$ is the design matrix with entries $I_i,j$ that are equal to 1 if subject $i$ is allocated to dose $j$ , and $0$ otherwise. The $sigma2W$ is the variance of the efficacy responses which can be either a fixed number or a number from an inverse gamma distribution. This flexible form aims to capture different shapes of the dose-efficacy curve. In addition, the first (RW1) or second order (RW2) random walk model can be used for smoothing data. That is the random walk model is used to model the first or the second order differences of the mean efficacy responses to its neighboring dose levels of their mean efficacy responses.

The RW1 model is given as

$betaW_j - betaW_j-1) ~ Normal(0, sigma2betaW),$

and for RW2 as

$betaW_j-2 - 2 * betaW_j-1 + beta_j ~ Normal(0, sigma2betaW),$

where $betaW_j$ is the vector of mean efficacy responses at dose j, and the $sigma2betaW$ is the prior variance which can be either a fixed number or a number from an inverse gamma distribution.

The eff and eff_dose are the pseudo efficacy responses and dose levels at which these pseudo efficacy responses are observed. Both, eff and eff_dose must be vectors of length at least 2. The positions of the elements specified in eff and eff_dose must correspond to each other between these vectors.

Usage

EffFlexi(eff, eff_dose, sigma2W, sigma2betaW, rw1 = TRUE, data)

.DefaultEffFlexi()
EffFlexi(eff, eff_dose, sigma2W, sigma2betaW, rw1 = TRUE, data)

.DefaultEffFlexi()

Arguments

eff

(numeric)
the pseudo efficacy responses. Elements of eff must correspond to the elements of eff_dose.

eff_dose

(numeric)
dose levels that correspond to pseudo efficacy responses in eff.

sigma2W

(numeric)
the prior variance of the efficacy responses. This is either a fixed value or a named vector with two positive numbers, the shape (a), and the rate (b) parameters for the inverse gamma distribution.

sigma2betaW

(numeric)
the prior variance of the random walk model used for smoothing. This is either a fixed value or a named vector with two positive numbers, the shape (a), and the rate (b) parameters for the inverse gamma distribution.

rw1

(flag)
used for smoothing data for this efficacy model. If it is TRUE, the first-order random walk model is used for the mean efficacy responses. Otherwise, the random walk of second order is used.

data

(DataDual)
observed data to update estimates of the model parameters.

Details

This model will output the updated value or the updated values of the parameters of the inverse gamma distributions for $sigma2W$ and $sigma2betaW$ . The EffFlexi inherits all slots from ModelEff class.

Slots

eff: (numeric)
the pseudo efficacy responses. Each element here must represent responses treated based on one subject. It must be a vector of length at least 2 and the order of its elements must correspond to values specified in eff_dose.
eff_dose: (numeric)
the pseudo efficacy dose levels at which the pseudo efficacy responses are observed. It must be a vector of length at least 2 and the order of its elements must correspond to values specified in eff.
sigma2W: (numeric)
the prior variance of the flexible efficacy form. This is either a fixed value or a named vector with two positive numbers, the shape (a), and the rate (b) parameters for the gamma distribution.
sigma2betaW: (numeric)
the prior variance of the random walk model for the mean efficacy responses. This is either a fixed value or a named vector with two positive numbers, the shape (a), and the rate (b) parameters for the gamma distribution.
use_fixed: (logical)
indicates whether a fixed value for sigma2W and sigma2betaW (for each parameter separately) is used or not. This slot is needed for internal purposes and must not be touched by the user.
rw1: (flag)
used for smoothing data for this efficacy model. If it is TRUE, the first-order random walk model is used for the mean efficacy responses. Otherwise, the random walk of second order is used.
X: (matrix)
the design matrix for the efficacy responses. It is based on both the pseudo and the observed efficacy responses.
RW: (matrix)
the difference matrix for the random walk model. This slot is needed for internal purposes and must not be used by the user.
RW_rank: (integer)
is the rank of the difference matrix. This slot is needed for internal purposes and must not be used by the user.

Note

Typically, end users will not use the .DefaultEffFlexi() function.

Examples

# Obtain prior estimates for the efficacy model in flexible form, given the pseudo data.
# First define an empty data set by defining the dose levels used in the study.
# There are 12 dose levels used in the study, ranging from 25 to 300 mg with
# increments of 25.
emptydata <- DataDual(doseGrid = seq(25, 300, 25))

# Define the pseudo data, i.e.: fixed 2 dose levels 25 and 300 mg (`eff_dose`)
# and the efficacy responses 1.223 and 2.513 observed at these two dose levels (`eff`).
# The prior variance of the pseudo efficacy responses can be either a fixed value
# or two parameters for the inverse gamma distribution, the shape (a) and the
# rate (b) (`sigma2W`).
# The prior variance of the random walk model can be either a fixed value or two
# parameters for the inverse gamma distribution, the shape (a) and the rate (b)
# (`sigma2betaW`).
my_model <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = emptydata
)

# Obtain estimates from the model given some observed data is available.
data <- DataDual(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = emptydata@doseGrid
)

my_model1 <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)
# Obtain prior estimates for the efficacy model in flexible form, given the pseudo data.
# First define an empty data set by defining the dose levels used in the study.
# There are 12 dose levels used in the study, ranging from 25 to 300 mg with
# increments of 25.
emptydata <- DataDual(doseGrid = seq(25, 300, 25))

# Define the pseudo data, i.e.: fixed 2 dose levels 25 and 300 mg (`eff_dose`)
# and the efficacy responses 1.223 and 2.513 observed at these two dose levels (`eff`).
# The prior variance of the pseudo efficacy responses can be either a fixed value
# or two parameters for the inverse gamma distribution, the shape (a) and the
# rate (b) (`sigma2W`).
# The prior variance of the random walk model can be either a fixed value or two
# parameters for the inverse gamma distribution, the shape (a) and the rate (b)
# (`sigma2betaW`).
my_model <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = emptydata
)

# Obtain estimates from the model given some observed data is available.
data <- DataDual(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = emptydata@doseGrid
)

my_model1 <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)

Computing Expected Efficacy for a Given Dose, Model and Samples

Description

A function that computes the value of expected efficacy at a specified dose level, based on the model specific parameters. The model parameters (samples) are obtained based on prior specified in form of pseudo data combined with observed responses (if any).

Usage

efficacy(dose, model, samples, ...)

## S4 method for signature 'numeric,Effloglog,Samples'
efficacy(dose, model, samples)

## S4 method for signature 'numeric,Effloglog,missing'
efficacy(dose, model)

## S4 method for signature 'numeric,EffFlexi,Samples'
efficacy(dose, model, samples)
efficacy(dose, model, samples, ...)

## S4 method for signature 'numeric,Effloglog,Samples'
efficacy(dose, model, samples)

## S4 method for signature 'numeric,Effloglog,missing'
efficacy(dose, model)

## S4 method for signature 'numeric,EffFlexi,Samples'
efficacy(dose, model, samples)

Arguments

dose

(numeric)
the dose which is targeted. The following recycling rule applies when samples is not missing: vectors of size 1 will be recycled to the size of the sample (i.e. size(samples)). Otherwise, dose must have the same size as the sample.

model

(ModelEff)
the efficacy model with pseudo data prior.

samples

(Samples)
samples of model's parameters that will be used to compute expected efficacy values. Can also be missing for some models.

...

model specific parameters when samples are not used.

Details

The efficacy() function computes the expected efficacy for given doses, using samples of the model parameter(s). If you work with multivariate model parameters, then assume that your model specific efficacy() method receives a samples matrix where the rows correspond to the sampling index, i.e. the layout is then ⁠nSamples x dimParameter⁠.

Value

A numeric vector with the values of expected efficacy. If non-scalar samples were used, then every element in the returned vector corresponds to one element of a sample. Hence, in this case, the output vector is of the same length as the sample vector. If scalar samples were used or no samples were used, e.g. for pseudo DLE/toxicity model, then the output is of the same length as the length of the dose.

Functions

efficacy(dose = numeric, model = Effloglog, samples = Samples): compute the expected efficacy at a specified dose level, based on the samples of Effloglog model parameters.
efficacy(dose = numeric, model = Effloglog, samples = missing): compute the expected efficacy at a specified dose level, based on the Effloglog model parameters. All model parameters (except dose) should be present in the model object.
efficacy(dose = numeric, model = EffFlexi, samples = Samples): compute the expected efficacy at a specified dose level, based on the samples of EffFlexi model parameters. If a given dose in the dose vector is from outside of the dose grid range, the NA_real is returned for this dose and the warning is thrown.

Examples

# Obtain the expected efficacy value for a given dose, a given pseudo efficacy
# model (in flexible form for prior) and efficacy samples.

# Empty data (i.e. no observed data), dose grid only.
my_data <- DataDual(doseGrid = seq(25, 300, 25))

my_model <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = my_data
)

my_options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 200,
  rng_kind = "Mersenne-Twister",
  rng_seed = 94
)

my_samples <- mcmc(data = my_data, model = my_model, options = my_options)

# Efficacy for dose 75.
efficacy(dose = 75, model = my_model, samples = my_samples)

# Obtain the expected efficacy value for a given dose, a given pseudo efficacy
# model (linear log-log efficacy) and no samples.
my_model_ll <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data,
  const = 0
)

efficacy(dose = 75, model = my_model_ll)
# Obtain the expected efficacy value for a given dose, a given pseudo efficacy
# model (in flexible form for prior) and efficacy samples.

# Empty data (i.e. no observed data), dose grid only.
my_data <- DataDual(doseGrid = seq(25, 300, 25))

my_model <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = my_data
)

my_options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 200,
  rng_kind = "Mersenne-Twister",
  rng_seed = 94
)

my_samples <- mcmc(data = my_data, model = my_model, options = my_options)

# Efficacy for dose 75.
efficacy(dose = 75, model = my_model, samples = my_samples)

# Obtain the expected efficacy value for a given dose, a given pseudo efficacy
# model (linear log-log efficacy) and no samples.
my_model_ll <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data,
  const = 0
)

efficacy(dose = 75, model = my_model_ll)

Getting the Efficacy Function for a Given Model Type

Description

A function that returns an efficacy() function that computes expected efficacy for a given dose level, based on the model specific parameters.

Usage

efficacyFunction(model, ...)

## S4 method for signature 'ModelEff'
efficacyFunction(model, ...)
efficacyFunction(model, ...)

## S4 method for signature 'ModelEff'
efficacyFunction(model, ...)

Arguments

model

(ModelEff)
the model.

...

model specific parameters.

Value

A efficacy() function that computes expected efficacy.

Functions

efficacyFunction(ModelEff):

Examples

my_data <- DataDual(
  doseGrid = c(0.001, seq(25, 300, 25)),
  placebo = TRUE
)

my_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data,
  const = 2
)

eff_fun <- efficacyFunction(my_model, theta1 = -4.8, theta2 = 3.7)
eff_fun(30)
my_data <- DataDual(
  doseGrid = c(0.001, seq(25, 300, 25)),
  placebo = TRUE
)

my_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data,
  const = 2
)

eff_fun <- efficacyFunction(my_model, theta1 = -4.8, theta2 = 3.7)
eff_fun(30)

`Effloglog`

Description

Effloglog is the class for the linear log-log efficacy model using pseudo data prior. It describes the relationship between continuous efficacy responses and corresponding dose levels in log-log scale. This efficacy log-log model is given as

$y_i = theta1 + theta2 * log(log(x_i)) + epsilon_i,$

where $y_i$ is the efficacy response for subject $i$ , $x_i$ is the dose level treated for subject $i$ and $epsilon_i$ is the random error term of efficacy model at subject $i$ . The error term $epsilon_i$ is a random variable that follows normal distribution with mean $0$ and variance $nu^{-1}$ , which is assumed to be the same for all subjects. There are three parameters in this model, the intercept $theta1$ , the slope $theta2$ and the precision $nu$ of the efficacy responses, also known as the inverse of the variance of the pseudo efficacy responses. It can be a fixed constant or having a gamma distribution. Therefore, a single scalar value or a vector with two positive numbers values must be specified for nu slot. If there are some observed efficacy responses available, in the output, nu will display the updated value of the precision or the updated values for the parameters of the gamma distribution. The Effloglog inherits all slots from ModelEff class.

Usage

Effloglog(eff, eff_dose, nu, data, const = 0)

.DefaultEffloglog()
Effloglog(eff, eff_dose, nu, data, const = 0)

.DefaultEffloglog()

Arguments

eff

(numeric)
the pseudo efficacy responses. Elements of eff must correspond to the elements of eff_dose.

eff_dose

(numeric)
dose levels that correspond to pseudo efficacy responses in eff.

nu

(numeric)
the precision (inverse of the variance) of the efficacy responses. This is either a fixed value or a named vector with two positive numbers, the shape (a), and the rate (b) parameters for the gamma distribution.

data

(DataDual)
observed data to update estimates of the model parameters.

const

(number)
the constant value added to the dose level when the dose level value is less than or equal to 1 and a special form of the linear log-log has to be applied (Yeung et al. 2015).

Details

The prior of this model is specified in form of pseudo data. First, at least two dose levels are fixed. Then, using e.g. experts' opinion, the efficacy values that correspond to these dose levels can be obtained, The eff and eff_dose arguments represent the prior in form of the pseudo data. The eff represents the pseudo efficacy values. The eff_dose represents the dose levels at which these pseudo efficacy values are observed. Hence, the positions of the elements specified in eff and eff_dose must correspond to each other between these vectors. Since at least 2 pseudo efficacy values are needed to obtain modal estimates of the intercept and slope parameters, both eff and eff_dose must be vectors of length at least 2.

The joint prior distribution of the intercept $theta1$ and the slope $theta2$ of this model follows bivariate normal distribution with mean $mu$ and covariance matrix $(nu * Q)^{-1}$ . The mean $mu$ is a $2 x 1$ column vector that contains the prior modal estimates of the intercept and the slope. Scalar $nu$ is the precision of the pseudo efficacy responses and $Q$ is the prior or posterior (given that observed, no DLT data is available) precision matrix. It is specified as $Q = X0^T * X0 + X^T * X$ , where $X0$ is a design matrix that is based on pseudo dose levels only, and $X$ is a design matrix that is based on dose levels corresponding to the no DLT efficacy responses observed only (if any). Hence, the $X0$ (or $X$ ) will be of size $r x 2$ , if there are $r >= 2$ pseudo efficacy responses specified (or if there are $r$ no DLT efficacy responses observed in the data).

Slots

eff: (numeric)
the pseudo efficacy responses. Each element here must represent responses treated based on one subject. It must be a vector of length at least 2 and the order of its elements must correspond to values specified in eff_dose.
eff_dose: (numeric)
the pseudo efficacy dose levels at which the pseudo efficacy responses are observed. It must be a vector of length at least 2 and the order of its elements must correspond to values specified in eff.
nu: (numeric)
parameter of the prior precision of pseudo efficacy responses. This is either a fixed value or a named vector with two positive numbers, the shape (a), and the rate (b) parameters for the gamma distribution.
use_fixed: (flag)
indicates whether nu specified is a fixed value or a vector with two parameters for gamma distribution. This slot is for internal purposes only and must not be used by the user.
theta1: (number)
the intercept in this efficacy log-log model. This slot is used in output to display the resulting prior or posterior modal estimates obtained based on the pseudo and observed (if any) data.
theta2: (number)
the slope in this efficacy log-log model. This slot is used in output to display the resulting prior or posterior modal estimates obtained based on the pseudo and observed (if any) data.
Pcov: (matrix)
refers to the $2 x 2$ covariance matrix of the estimators of the intercept $theta1$ and the slope $theta2$ parameters in this model. This is used in output to display the resulting prior and posterior covariance matrix of $theta1$ and $theta2$ obtained, based on the pseudo and observed (if any) data. This slot is needed for internal purposes.
X: (matrix)
is the design matrix that is based on either the pseudo dose levels or observed dose levels (without DLT). This is used in the output to display the design matrix for the pseudo or the observed efficacy responses.
Y: (numeric)
is a vector that either contains the pseudo efficacy responses or observed efficacy responses (without DLT).
mu: (numeric)
a vector of the prior or the posterior modal estimates of the intercept ( $theta1$ ) and the slope ( $theta2$ ). This slot is used in output to display as the mean of the prior or posterior bivariate normal distribution for $theta1$ and $theta2$ .
Q: (matrix)
is the prior or posterior (given that observed, no DLT data is available) precision matrix. It is specified as $Q = X0^T * X0 + X^T * X$ , where $X0$ is a design matrix that is based on pseudo dose levels only, and $X$ is a design matrix that is based on dose levels corresponding to the observed, no DLT efficacy values only (if any).
const: (number)
a non-negative number (default to 0), leading to the model form described above. In general, the model has the form $y_i = theta1 + theta2 * log(log(x_i + const)) + epsilon_i$ , such that dose levels greater than $1 - const$ can be considered as described in Yeung et al. (2015).

Note

Typically, end users will not use the .DefaultEffloglog() function.

References

Yeung WY, Whitehead J, Reigner B, Beyer U, Diack C, Jaki T (2015). “Bayesian adaptive dose-escalation procedure for binary and continuous responses utilizing a gain function.” Pharmaceutical Statistics. doi:10.1002/pst.1706. Published online ahead of print.

Examples

# Obtain prior modal estimates given the pseudo data.
# First we use an empty data set such that only the dose levels under
# investigations are given. In total, 12 dose levels are under investigation
# ranging from 25 to 300 mg with increments of 25 (i.e 25, 50, 75, ..., 300).
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)

# Define the pseudo data as first by fixing two dose levels 25 and 300 mg (`eff_dose`).
# Then, the efficacy responses observed at these two dose levels are 1.223 and 2.513 (`eff`).
# We specify the prior precision of the pseudo efficacy responses (`nu`) as a vector
# with the shape (a) and the rate (b) parameters for the gamma distribution.
# Obtain modal estimates and other estimates from the model (no observations,
# only pseudo data).
my_model1 <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# Observed data.
my_data <- DataDual(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = emptydata@doseGrid
)

# Obtain posterior modal estimates and other estimates from the model given some
# observed data.
my_model2 <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data
)
# Obtain prior modal estimates given the pseudo data.
# First we use an empty data set such that only the dose levels under
# investigations are given. In total, 12 dose levels are under investigation
# ranging from 25 to 300 mg with increments of 25 (i.e 25, 50, 75, ..., 300).
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)

# Define the pseudo data as first by fixing two dose levels 25 and 300 mg (`eff_dose`).
# Then, the efficacy responses observed at these two dose levels are 1.223 and 2.513 (`eff`).
# We specify the prior precision of the pseudo efficacy responses (`nu`) as a vector
# with the shape (a) and the rate (b) parameters for the gamma distribution.
# Obtain modal estimates and other estimates from the model (no observations,
# only pseudo data).
my_model1 <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# Observed data.
my_data <- DataDual(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = emptydata@doseGrid
)

# Obtain posterior modal estimates and other estimates from the model given some
# observed data.
my_model2 <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data
)

Verbose Logging

Description

A family of wrappers of selected futile.logger::futile.logger functions that control the logging mechanism in crmPack. The crmPack uses futile.logger::futile.logger package for the logging purposes. All the messages logged in crmPack are logged into crmPack logger at the futile.logger::TRACE level. Hence, enabling verbose logging means that the logging threshold will be set to futile.logger::TRACE for the crmPack logger, and disabling verbose logging means that it will be set to futile.logger::FATAL.

Usage

enable_logging()

disable_logging()

is_logging_enabled()

log_trace(msg, ..., capture = FALSE)
enable_logging()

disable_logging()

is_logging_enabled()

log_trace(msg, ..., capture = FALSE)

Arguments

msg

The message to log

...

Optional arguments to populate the format string

capture

Capture print output of variables instead of interpolate

Functions

enable_logging(): A simple wrapper of futile.logger::flog.threshold() that enables crmPack verbose logging by setting logging threshold to futile.logger::TRACE for crmPack logger.
disable_logging(): A simple wrapper of futile.logger::flog.threshold() that disables crmPack verbose logging by setting logging threshold to futile.logger::FATAL for crmPack logger.
is_logging_enabled(): A simple wrapper of futile.logger::flog.logger() that checks whether current threshold level for crmPack logger is verbose, which is futile.logger::TRACE. It returns TRUE if the current logging level is verbose, FALSE otherwise.
log_trace(): A simple wrapper of futile.logger::flog.trace() that prints a log message in the crmPack logger.

Obtain Hypothetical Trial Course Table for a Design

Description

This generic function takes a design and generates a data.frame showing the beginning of several hypothetical trial courses under the design. This means, from the generated data.frame one can read off:

Usage

examine(object, ..., maxNoIncrement = 100L)

## S4 method for signature 'Design'
examine(object, mcmcOptions = McmcOptions(), ..., maxNoIncrement)

## S4 method for signature 'RuleDesign'
examine(object, ..., maxNoIncrement = 100L)

## S4 method for signature 'DADesign'
examine(object, mcmcOptions = McmcOptions(), ..., maxNoIncrement)
examine(object, ..., maxNoIncrement = 100L)

## S4 method for signature 'Design'
examine(object, mcmcOptions = McmcOptions(), ..., maxNoIncrement)

## S4 method for signature 'RuleDesign'
examine(object, ..., maxNoIncrement = 100L)

## S4 method for signature 'DADesign'
examine(object, mcmcOptions = McmcOptions(), ..., maxNoIncrement)

Arguments

object

(Design or RuleDesign)
the design we want to examine

...

additional arguments (see methods)

maxNoIncrement

maximum number of contiguous next doses at 0 DLTs that are the same as before, i.e. no increment (default to 100)

mcmcOptions

(McmcOptions)
giving the MCMC options for each evaluation in the trial. By default, the standard options are used

Details

how many cohorts are required in the optimal case (no DLTs observed) in order to reach the highest dose of the specified dose grid (or until the stopping rule is fulfilled)
assuming no DLTs are observed until a certain dose level, what the next recommended dose is for all possible number of DLTs observed
the actual relative increments that will be used in these cases
whether the trial would stop at a certain cohort

Examining the "single trial" behavior of a dose escalation design is the first important step in evaluating a design, and cannot be replaced by studying solely the operating characteristics in "many trials". The cohort sizes are also taken from the design, assuming no DLTs occur until the dose listed.

Value

The data frame

Functions

examine(Design): Examine a model-based CRM.
examine(RuleDesign): Examine a rule-based design.
examine(DADesign): Examine a model-based CRM.

Examples

# Define the dose-grid.
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25))


# Initialize the CRM model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)


my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- (my_stopping1 & my_stopping2) | my_stopping3 | StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
my_design <- Design(
  model = my_model,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 3
)

my_options <- McmcOptions(
  burnin = 10,
  step = 1,
  samples = 20,
  rng_kind = "Super-Duper",
  rng_seed = 94
)


examine(my_design, my_options)


# Example where examine stops because stopping rule already fulfilled.
my_stopping4 <- StoppingMinPatients(nPatients = 3)
my_stopping <- (my_stopping1 & my_stopping2) | my_stopping4

my_design <- Design(
  model = my_model,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 3
)


examine(my_design, mcmcOptions = my_options)


# Example where examine stops because infinite looping
# (note that here a very low threshold is used for the parameter
# "maxNoIncrement" in "examine" to keep the execution time short).
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.00001)
)

my_stopping <- (my_stopping1 & my_stopping2) | StoppingMissingDose()

design <- Design(
  model = my_model,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 3
)


examine(my_design, mcmcOptions = my_options, maxNoIncrement = 2)

# Define the dose-grid
emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(
  nextBest = NextBestThreePlusThree(),
  cohort_size = CohortSizeConst(size = 3L),
  data = emptydata,
  startingDose = 5
)

# Examine the design
set.seed(4235)

examine(myDesign)

# nolint start

# Define the dose-grid and PEM parameters
emptydata <- DataDA(doseGrid = c(
  0.1, 0.5, 1, 1.5, 3, 6,
  seq(from = 10, to = 80, by = 2)
), Tmax = 60)
# Initialize the mDA-CRM model
npiece_ <- 10
Tmax_ <- 60

lambda_prior <- function(k) {
  npiece_ / (Tmax_ * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(as.matrix(c(1:npiece_), 1, npiece_), 2, lambda_prior))),
  c_par = 2
)
# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping2 <- StoppingMinPatients(nPatients = 50)

myStopping <- (myStopping1 | myStopping2) | StoppingMissingDose()

# Choose the safety window
mysafetywindow <- SafetyWindowConst(c(6, 2), 7, 7)

# Initialize the design
design <- DADesign(
  model = model,
  increments = myIncrements,
  nextBest = myNextBest,
  stopping = myStopping,
  cohort_size = mySize,
  data = emptydata,
  safetyWindow = mysafetywindow,
  startingDose = 3
)

set.seed(4235)
# MCMC parameters are set to small values only to show this example. They should be
# increased for a real case.
# This procedure will take a while.
options <- McmcOptions(
  burnin = 10,
  step = 1,
  samples = 100,
  rng_kind = "Mersenne-Twister",
  rng_seed = 12
)

testthat::expect_warning(
  result <- examine(design, mcmcOptions = options, maxNoIncrement = 2),
  "Stopping because 2 times no increment"
)


# nolint end
# Define the dose-grid.
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25))


# Initialize the CRM model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)


my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- (my_stopping1 & my_stopping2) | my_stopping3 | StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
my_design <- Design(
  model = my_model,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 3
)

my_options <- McmcOptions(
  burnin = 10,
  step = 1,
  samples = 20,
  rng_kind = "Super-Duper",
  rng_seed = 94
)


examine(my_design, my_options)


# Example where examine stops because stopping rule already fulfilled.
my_stopping4 <- StoppingMinPatients(nPatients = 3)
my_stopping <- (my_stopping1 & my_stopping2) | my_stopping4

my_design <- Design(
  model = my_model,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 3
)


examine(my_design, mcmcOptions = my_options)


# Example where examine stops because infinite looping
# (note that here a very low threshold is used for the parameter
# "maxNoIncrement" in "examine" to keep the execution time short).
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.00001)
)

my_stopping <- (my_stopping1 & my_stopping2) | StoppingMissingDose()

design <- Design(
  model = my_model,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 3
)


examine(my_design, mcmcOptions = my_options, maxNoIncrement = 2)

# Define the dose-grid
emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(
  nextBest = NextBestThreePlusThree(),
  cohort_size = CohortSizeConst(size = 3L),
  data = emptydata,
  startingDose = 5
)

# Examine the design
set.seed(4235)

examine(myDesign)

# nolint start

# Define the dose-grid and PEM parameters
emptydata <- DataDA(doseGrid = c(
  0.1, 0.5, 1, 1.5, 3, 6,
  seq(from = 10, to = 80, by = 2)
), Tmax = 60)
# Initialize the mDA-CRM model
npiece_ <- 10
Tmax_ <- 60

lambda_prior <- function(k) {
  npiece_ / (Tmax_ * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(as.matrix(c(1:npiece_), 1, npiece_), 2, lambda_prior))),
  c_par = 2
)
# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping2 <- StoppingMinPatients(nPatients = 50)

myStopping <- (myStopping1 | myStopping2) | StoppingMissingDose()

# Choose the safety window
mysafetywindow <- SafetyWindowConst(c(6, 2), 7, 7)

# Initialize the design
design <- DADesign(
  model = model,
  increments = myIncrements,
  nextBest = myNextBest,
  stopping = myStopping,
  cohort_size = mySize,
  data = emptydata,
  safetyWindow = mysafetywindow,
  startingDose = 3
)

set.seed(4235)
# MCMC parameters are set to small values only to show this example. They should be
# increased for a real case.
# This procedure will take a while.
options <- McmcOptions(
  burnin = 10,
  step = 1,
  samples = 100,
  rng_kind = "Mersenne-Twister",
  rng_seed = 12
)

testthat::expect_warning(
  result <- examine(design, mcmcOptions = options, maxNoIncrement = 2),
  "Stopping because 2 times no increment"
)


# nolint end

Fit method for the Samples class

Description

Note this new generic function is necessary because the fitted function only allows the first argument object to appear in the signature. But we need also other arguments in the signature.

Usage

fit(object, model, data, ...)

## S4 method for signature 'Samples,GeneralModel,Data'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,DualEndpoint,DataDual'
fit(object, model, data, quantiles = c(0.025, 0.975), middle = mean, ...)

## S4 method for signature 'Samples,LogisticIndepBeta,Data'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,Effloglog,DataDual'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,EffFlexi,DataDual'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,LogisticLogNormalOrdinal,DataOrdinal'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)
fit(object, model, data, ...)

## S4 method for signature 'Samples,GeneralModel,Data'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,DualEndpoint,DataDual'
fit(object, model, data, quantiles = c(0.025, 0.975), middle = mean, ...)

## S4 method for signature 'Samples,LogisticIndepBeta,Data'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,Effloglog,DataDual'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,EffFlexi,DataDual'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,LogisticLogNormalOrdinal,DataOrdinal'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

Arguments

object

the Samples object

model

the GeneralModel object

data

the Data object

...

passed down to the prob() method.

points

at which dose levels is the fit requested? default is the dose grid

quantiles

the quantiles to be calculated (default: 0.025 and 0.975)

middle

the function for computing the middle point. Default: mean

Value

the data frame with required information (see method details)

Functions

fit(object = Samples, model = GeneralModel, data = Data): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-toxicity curve
fit(object = Samples, model = DualEndpoint, data = DataDual): This method returns a data frame with dose, and middle, lower and upper quantiles, for both the dose-tox and dose-biomarker (suffix "Biomarker") curves, for all grid points (Note that currently only the grid points can be used, because the DualEndpointRW models only allow that)
fit(object = Samples, model = LogisticIndepBeta, data = Data): This method return a data frame with dose, middle lower and upper quantiles for the dose-DLE curve using DLE samples for “LogisticIndepBeta” model class
fit(object = Samples, model = Effloglog, data = DataDual): This method returns a data frame with dose, middle, lower, upper quantiles for the dose-efficacy curve using efficacy samples for “Effloglog” model class
fit(object = Samples, model = EffFlexi, data = DataDual): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-efficacy curve using efficacy samples for “EffFlexi” model class
fit(object = Samples, model = LogisticLogNormalOrdinal, data = DataOrdinal): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-efficacy curve using efficacy samples for the “LogisticLogNormalOrdinal” model class

Examples

# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses
fitted <- fit(
  object = samples,
  model = model,
  data = data,
  quantiles = c(0.025, 0.975),
  middle = mean
)


# ----------------------------------------------
# A different example using a different model
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)


model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## samples must be from 'Samples' class (object slot in fit)
samples <- mcmc(data, model, options)

fitted <- fit(object = samples, model = model, data = data)

# nolint end
# nolint start

# Create some data
data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 500)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
fitted <- fit(
  object = samples,
  model = model,
  data = data,
  quantiles = c(0.025, 0.975),
  middle = mean
)

# nolint end
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-DLE curve
## at all dose levels using a DLE sample, a DLE model and the data
## samples must be from 'Samples' class (object slot)
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)
## model must be from 'Model' or 'ModelTox' class e.g using 'LogisticIbdepBeta' model class
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
##options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## samples must be from 'Samples' class (object slot in fit)
samples <- mcmc(data, model, options)

fit(object = samples, model = model, data = data)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, a pseudo efficacy model and the data
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel <- Effloglog(
  c(1.223, 2.513),
  c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  c = 0
)
## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)
fit(object = Effsamples, model = Effmodel, data = data)
# nolint start

##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, the 'EffFlexi' efficacy model and the data
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)

## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)
fit(object = Effsamples, model = Effmodel, data = data)

# nolint end
model <- .DefaultLogisticLogNormalOrdinal()
ordinal_data <- .DefaultDataOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(ordinal_data, model, options)

grade1_fit <- fit(samples, model, ordinal_data, grade = 1L)
grade2_fit <- fit(samples, model, ordinal_data, grade = 2L)
# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses
fitted <- fit(
  object = samples,
  model = model,
  data = data,
  quantiles = c(0.025, 0.975),
  middle = mean
)


# ----------------------------------------------
# A different example using a different model
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)


model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## samples must be from 'Samples' class (object slot in fit)
samples <- mcmc(data, model, options)

fitted <- fit(object = samples, model = model, data = data)

# nolint end
# nolint start

# Create some data
data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 500)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
fitted <- fit(
  object = samples,
  model = model,
  data = data,
  quantiles = c(0.025, 0.975),
  middle = mean
)

# nolint end
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-DLE curve
## at all dose levels using a DLE sample, a DLE model and the data
## samples must be from 'Samples' class (object slot)
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)
## model must be from 'Model' or 'ModelTox' class e.g using 'LogisticIbdepBeta' model class
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
##options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## samples must be from 'Samples' class (object slot in fit)
samples <- mcmc(data, model, options)

fit(object = samples, model = model, data = data)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, a pseudo efficacy model and the data
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel <- Effloglog(
  c(1.223, 2.513),
  c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  c = 0
)
## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)
fit(object = Effsamples, model = Effmodel, data = data)
# nolint start

##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, the 'EffFlexi' efficacy model and the data
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)

## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)
fit(object = Effsamples, model = Effmodel, data = data)

# nolint end
model <- .DefaultLogisticLogNormalOrdinal()
ordinal_data <- .DefaultDataOrdinal()
options <- .DefaultMcmcOptions()
samples <- mcmc(ordinal_data, model, options)

grade1_fit <- fit(samples, model, ordinal_data, grade = 1L)
grade2_fit <- fit(samples, model, ordinal_data, grade = 2L)

Get the fitted values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples

Description

Get the fitted values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples

Usage

fitGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples,DataDual'
fitGain(
  DLEmodel,
  DLEsamples,
  Effmodel,
  Effsamples,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)
fitGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples,DataDual'
fitGain(
  DLEmodel,
  DLEsamples,
  Effmodel,
  Effsamples,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

Arguments

DLEmodel

the DLE pseudo model of ModelTox class object

DLEsamples

the DLE samples of Samples class object

Effmodel

the efficacy pseudo model of ModelEff class object

Effsamples

the efficacy samples of Samples class object

data

the data input of DataDual class object

...

additional arguments for methods

points

at which dose levels is the fit requested? default is the dose grid

quantiles

the quantiles to be calculated (default: 0.025 and 0.975)

middle

the function for computing the middle point. Default: mean

Functions

fitGain( DLEmodel = ModelTox, DLEsamples = Samples, Effmodel = ModelEff, Effsamples = Samples, data = DataDual ): This method returns a data frame with dose, middle, lower, upper quantiles for the gain values obtained given the DLE and the efficacy samples

Examples

##Obtain the 'fitGain' the middle, uppper and lower quantiles for the samples of gain values
## at all dose levels using a pseudo DLE model, a DLE sample, a pseudo Efficacy model and
## a efficacy sample
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## DLE model must be from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## Efficacy model must be from 'ModelEff' class e.g using 'Effloglog' model
Effmodel <- Effloglog(
  c(1.223, 2.513),
  c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  c = 0
)
## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x = data@x, y = data@y, doseGrid = data@doseGrid)

DLEsamples <- mcmc(data = data1, model = DLEmodel, options = options)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)

fitGain(
  DLEmodel = DLEmodel,
  DLEsamples = DLEsamples,
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  data = data
)
##Obtain the 'fitGain' the middle, uppper and lower quantiles for the samples of gain values
## at all dose levels using a pseudo DLE model, a DLE sample, a pseudo Efficacy model and
## a efficacy sample
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## DLE model must be from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## Efficacy model must be from 'ModelEff' class e.g using 'Effloglog' model
Effmodel <- Effloglog(
  c(1.223, 2.513),
  c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  c = 0
)
## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x = data@x, y = data@y, doseGrid = data@doseGrid)

DLEsamples <- mcmc(data = data1, model = DLEmodel, options = options)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)

fitGain(
  DLEmodel = DLEmodel,
  DLEsamples = DLEsamples,
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  data = data
)
##Obtain the 'fitGain' the middle, uppper and lower quantiles for the samples of gain values
## at all dose levels using a pseudo DLE model, a DLE sample, a pseudo Efficacy model and
## a efficacy sample
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## DLE model must be from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## Efficacy model must be from 'ModelEff' class e.g using 'Effloglog' model
Effmodel <- Effloglog(
  c(1.223, 2.513),
  c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  c = 0
)
## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x = data@x, y = data@y, doseGrid = data@doseGrid)

DLEsamples <- mcmc(data = data1, model = DLEmodel, options = options)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)

fitGain(
  DLEmodel = DLEmodel,
  DLEsamples = DLEsamples,
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  data = data
)
##Obtain the 'fitGain' the middle, uppper and lower quantiles for the samples of gain values
## at all dose levels using a pseudo DLE model, a DLE sample, a pseudo Efficacy model and
## a efficacy sample
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## DLE model must be from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## Efficacy model must be from 'ModelEff' class e.g using 'Effloglog' model
Effmodel <- Effloglog(
  c(1.223, 2.513),
  c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  c = 0
)
## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x = data@x, y = data@y, doseGrid = data@doseGrid)

DLEsamples <- mcmc(data = data1, model = DLEmodel, options = options)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)

fitGain(
  DLEmodel = DLEmodel,
  DLEsamples = DLEsamples,
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  data = data
)

Get the fitted DLT free survival (piecewise exponential model). This function returns a data frame with dose, middle, lower and upper quantiles for the `PEM` curve. If hazard=TRUE,

Description

Get the fitted DLT free survival (piecewise exponential model). This function returns a data frame with dose, middle, lower and upper quantiles for the PEM curve. If hazard=TRUE,

Usage

fitPEM(
  object,
  model,
  data,
  quantiles = c(0.025, 0.975),
  middle = mean,
  hazard = FALSE,
  ...
)

## S4 method for signature 'Samples,DALogisticLogNormal,DataDA'
fitPEM(
  object,
  model,
  data,
  quantiles = c(0.025, 0.975),
  middle = mean,
  hazard = FALSE,
  ...
)
fitPEM(
  object,
  model,
  data,
  quantiles = c(0.025, 0.975),
  middle = mean,
  hazard = FALSE,
  ...
)

## S4 method for signature 'Samples,DALogisticLogNormal,DataDA'
fitPEM(
  object,
  model,
  data,
  quantiles = c(0.025, 0.975),
  middle = mean,
  hazard = FALSE,
  ...
)

Arguments

object

mcmc samples

model

the mDA-CRM model

data

the data input, a DataDA class object

quantiles

the quantiles to be calculated (default: 0.025 and 0.975)

middle

the function for computing the middle point. Default: mean

hazard

should the the hazard over time be plotted based on the PEM? (not default) Otherwise ...

...

additional arguments for methods

Functions

fitPEM(object = Samples, model = DALogisticLogNormal, data = DataDA): This method works for the DALogisticLogNormal model class.

Examples

# nolint start

# Create the data
data <- DataDA(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  ID = 1L:8L,
  cohort = as.integer(c(1:5, 6, 6, 6)),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60
)

# Initialize the CRM model used to model the data
npiece_ <- 10
lambda_prior <- function(k) {
  npiece_ / (data@Tmax * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(
    as.matrix(c(1:npiece_), 1, npiece_),
    2,
    lambda_prior
  ))),
  c_par = 2
)

# Obtain the posterior

options <- McmcOptions(
  burnin = 10,
  step = 2,
  samples = 1e2
)

set.seed(94)
samples <- mcmc(data, model, options)


# Extract the posterior mean hazard (and empirical 2.5 and 97.5 percentile)
# for the piecewise exponential model
# If hazard=FALSE, the posterior PEM will be plot
fitted <- fitPEM(
  object = samples,
  model = model,
  data = data,
  middle = mean,
  hazard = TRUE,
  quantiles = c(0.25, 0.75)
)

# nolint end
# nolint start

# Create the data
data <- DataDA(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  ID = 1L:8L,
  cohort = as.integer(c(1:5, 6, 6, 6)),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60
)

# Initialize the CRM model used to model the data
npiece_ <- 10
lambda_prior <- function(k) {
  npiece_ / (data@Tmax * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(
    as.matrix(c(1:npiece_), 1, npiece_),
    2,
    lambda_prior
  ))),
  c_par = 2
)

# Obtain the posterior

options <- McmcOptions(
  burnin = 10,
  step = 2,
  samples = 1e2
)

set.seed(94)
samples <- mcmc(data, model, options)


# Extract the posterior mean hazard (and empirical 2.5 and 97.5 percentile)
# for the piecewise exponential model
# If hazard=FALSE, the posterior PEM will be plot
fitted <- fitPEM(
  object = samples,
  model = model,
  data = data,
  middle = mean,
  hazard = TRUE,
  quantiles = c(0.25, 0.75)
)

# nolint end

`FractionalCRM`

Description

FractionalCRM is the class for a fractional CRM model based on a one parameter CRM (with normal prior on the log-power parameter) as well as Kaplan-Meier based estimation of the conditional probability to experience a DLT for non-complete observations.

This fractional CRM model follows the paper and code by Yin et al. (2013).

Usage

FractionalCRM(...)

.DefaultFractionalCRM()
FractionalCRM(...)

.DefaultFractionalCRM()

Arguments

...

Arguments passed on to OneParLogNormalPrior

skel_probs: (numeric)
skeleton prior probabilities. This is a vector of unique and sorted probability values between 0 and 1.
dose_grid: (numeric)
dose grid. It must be must be a sorted vector of the same length as skel_probs.
sigma2: (number)
prior variance of log power parameter alpha.

Note

Typically, end users will not use the .DefaultTITELogisticLogNormal() function.

References

Yin G, Zheng S, Xu J (2013). “Fractional dose-finding methods with late-onset toxicity in phase I clinical trials.” Journal of Biopharmaceutical Statistics, 23(4), 856–870. doi:10.1080/10543406.2013.789892.

Examples

my_model <- FractionalCRM(
  skel_probs = c(0.1, 0.2, 0.3, 0.4),
  dose_grid = c(10, 30, 50, 100),
  sigma2 = 2
)
my_model <- FractionalCRM(
  skel_probs = c(0.1, 0.2, 0.3, 0.4),
  dose_grid = c(10, 30, 50, 100),
  sigma2 = 2
)

Compute Gain Values based on Pseudo DLE and a Pseudo Efficacy Models and Using Optional Samples.

Description

Usage

gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...)

## S4 method for signature 'numeric,ModelTox,Samples,ModelEff,Samples'
gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...)

## S4 method for signature 'numeric,ModelTox,missing,Effloglog,missing'
gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...)
gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...)

## S4 method for signature 'numeric,ModelTox,Samples,ModelEff,Samples'
gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...)

## S4 method for signature 'numeric,ModelTox,missing,Effloglog,missing'
gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...)

Arguments

dose

(number or numeric)
the dose which is targeted. The following recycling rule applies when samples are not missing: vectors of size 1 will be recycled to the size of the sample. Otherwise, dose must have the same size as the sample.

model_dle

(ModelTox)
pseudo DLE (dose-limiting events)/toxicity model.

samples_dle

(Samples)
the samples of model's parameters that will be used to compute toxicity probabilities. Can also be missing for some models.

model_eff

(ModelEff)
the efficacy model with pseudo data prior.

samples_eff

(Samples)
samples of model's parameters that will be used to compute expected efficacy values. Can also be missing for some models.

...

not used.

Details

This function computes the gain values for a given dose level, pseudo DLE and Efficacy models as well as a given DLE and Efficacy samples.

Value

The gain values.

Functions

gain( dose = numeric, model_dle = ModelTox, samples_dle = Samples, model_eff = ModelEff, samples_eff = Samples ):
gain( dose = numeric, model_dle = ModelTox, samples_dle = missing, model_eff = Effloglog, samples_eff = missing ): Compute the gain value for a given dose level, pseudo DLE and Efficacy models without DLE and the Efficacy samples.

Examples

# Obtain the gain value for a given dose, a pseudo DLE and efficacy models
# as well as DLE and efficacy samples.
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
mcmc_opts <- McmcOptions(burnin = 100, step = 2, samples = 200)

# DLE model and samples.
model_dle <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

samples_dle <- mcmc(emptydata, model_dle, mcmc_opts)

# Efficacy model (Effloglog) and samples.
model_effloglog <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

samples_effloglog <- mcmc(emptydata, model_effloglog, mcmc_opts)

# Gain values for dose level 75 and Effloglog efficacy model.
gain(
  dose = 75,
  model_dle = model_dle,
  samples_dle = samples_dle,
  model_eff = model_effloglog,
  samples_eff = samples_effloglog
)

# Efficacy model (EffFlexi) and samples.
model_effflexi <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = emptydata
)

samples_effflexi <- mcmc(emptydata, model_effflexi, mcmc_opts)

# Gain values for dose level 75 and EffFlexi efficacy model.
gain(
  dose = 75,
  model_dle = model_dle,
  samples_dle = samples_dle,
  model_eff = model_effflexi,
  samples_eff = samples_effflexi
)
# Obtain the gain value for a given dose, a pseudo DLE and efficacy models
# without DLE and efficacy samples.
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
data <- Data(doseGrid = seq(25, 300, 25), placebo = FALSE)
mcmc_opts <- McmcOptions(burnin = 100, step = 2, samples = 200)

# DLE model and samples.
model_dle <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

# Efficacy model and samples.
model_eff <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# Gain value for dose level 75.
gain(
  dose = 75,
  model_dle = model_dle,
  model_eff = model_eff
)
# Obtain the gain value for a given dose, a pseudo DLE and efficacy models
# as well as DLE and efficacy samples.
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
mcmc_opts <- McmcOptions(burnin = 100, step = 2, samples = 200)

# DLE model and samples.
model_dle <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

samples_dle <- mcmc(emptydata, model_dle, mcmc_opts)

# Efficacy model (Effloglog) and samples.
model_effloglog <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

samples_effloglog <- mcmc(emptydata, model_effloglog, mcmc_opts)

# Gain values for dose level 75 and Effloglog efficacy model.
gain(
  dose = 75,
  model_dle = model_dle,
  samples_dle = samples_dle,
  model_eff = model_effloglog,
  samples_eff = samples_effloglog
)

# Efficacy model (EffFlexi) and samples.
model_effflexi <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = emptydata
)

samples_effflexi <- mcmc(emptydata, model_effflexi, mcmc_opts)

# Gain values for dose level 75 and EffFlexi efficacy model.
gain(
  dose = 75,
  model_dle = model_dle,
  samples_dle = samples_dle,
  model_eff = model_effflexi,
  samples_eff = samples_effflexi
)
# Obtain the gain value for a given dose, a pseudo DLE and efficacy models
# without DLE and efficacy samples.
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
data <- Data(doseGrid = seq(25, 300, 25), placebo = FALSE)
mcmc_opts <- McmcOptions(burnin = 100, step = 2, samples = 200)

# DLE model and samples.
model_dle <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

# Efficacy model and samples.
model_eff <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# Gain value for dose level 75.
gain(
  dose = 75,
  model_dle = model_dle,
  model_eff = model_eff
)

`GeneralData`

Description

GeneralData is a class for general data input.

Usage

.DefaultDataGeneral()
.DefaultDataGeneral()

Slots

ID: (integer)
unique patient IDs.
cohort: (integer)
the cohort (non-negative sorted) indices.
nObs: (integer)
number of observations, a single value.

Note

Typically, end users will not use the .DefaultDataGeneral() function.

`GeneralModel`

Description

GeneralModel is a general model class, from which all other specific model-like classes inherit.

Usage

.DefaultGeneralModel()
.DefaultGeneralModel()

Slots

datamodel: (function)
a function representing the JAGS data model specification.
priormodel: (function)
a function representing the JAGS prior specification.
modelspecs: (function)
a function computing the list of the data model and prior model specifications that are required to be specified completely (e.g. prior parameters, reference dose, etc.), based on the data slots that are required as arguments of this function. Apart of data arguments, this function can be specified with one additional (optional) argument from_prior of type logical and length one. This from_prior flag can be used to differentiate the output of the modelspecs, as its value is taken directly from the from_prior argument of the mcmc method that invokes modelspecs function. That is, when from_prior is TRUE, then only priormodel JAGS model is used (datamodel is not used) by the mcmc, and hence modelspecs function should return all the parameters that are required by the priormodel only. If the value of from_prior is FALSE, then both JAGS models datamodel and priormodel are used in the MCMC sampler, and hence modelspecs function should return all the parameters required by both datamodel and priormodel.
init: (function)
a function computing the list of starting values for parameters required to be initialized in the MCMC sampler, based on the data slots that are required as arguments of this function.
datanames: (character)
the names of all data slots that are used by datamodel JAGS function. No other names should be specified here.
datanames_prior: (character)
the names of all data slots that are used by priormodel JAGS function. No other names should be specified here.
sample: (character)
names of all parameters from which you would like to save the MCMC samples.

Note

The datamodel must obey the convention that the data input is called exactly in the same way as in the corresponding data class. All prior distributions for parameters should be contained in the model function priormodel. The background is that this can be used to simulate from the prior distribution, before obtaining any data.

Typically, end users will not use the .DefaultGeneralModel() function.

`GeneralSimulations`

Description

This class captures trial simulations. Here also the random generator state before starting the simulation is saved, in order to be able to reproduce the outcome. For this just use set.seed with the seed as argument before running simulate,Design-method.

Usage

GeneralSimulations(data, doses, seed)

.DefaultGeneralSimulations()
GeneralSimulations(data, doses, seed)

.DefaultGeneralSimulations()

Arguments

data

(list)
see slot definition.

doses

(numeric)
see slot definition.

seed

(integer)
see slot definition.

Slots

data: (list)
produced Data objects.
doses: (numeric)
final dose recommendations.
seed: (integer)
random generator state before starting the simulation.

Note

Typically, end users will not use the .DefaultGeneralSimulations() function.

Examples

data <- list(
  Data(x = 1:3, y = c(0, 1, 0), doseGrid = 1:3, ID = 1L:3L, cohort = 1L:3L),
  Data(x = 4:6, y = c(0, 1, 0), doseGrid = 4:6, ID = 1L:3L, cohort = 1L:3L)
)

doses <- c(1, 2)

seed <- 123L

simulations <- GeneralSimulations(data, doses, seed)
data <- list(
  Data(x = 1:3, y = c(0, 1, 0), doseGrid = 1:3, ID = 1L:3L, cohort = 1L:3L),
  Data(x = 4:6, y = c(0, 1, 0), doseGrid = 4:6, ID = 1L:3L, cohort = 1L:3L)
)

doses <- c(1, 2)

seed <- 123L

simulations <- GeneralSimulations(data, doses, seed)

`GeneralSimulationsSummary`

Description

This class captures the summary of general simulations output. Note that objects should not be created by users, therefore no initialization function is provided for this class.

Usage

.DefaultGeneralSimulationsSummary()
.DefaultGeneralSimulationsSummary()

Slots

target: (numeric)
target toxicity interval
target_dose_interval: (numeric)
corresponding target dose interval
nsim: (integer)
number of simulations
prop_dlts: (ANY)
A numeric array (multi-dimensional) or list representing proportions of DLTs in the trials
mean_tox_risk: (numeric)
mean toxicity risks for the patients
dose_selected: (numeric)
doses selected as MTD
tox_at_doses_selected: (numeric)
true toxicity at doses selected
prop_at_target: (numeric)
Proportion of trials selecting target MTD
dose_most_selected: (numeric)
dose most often selected as MTD
obs_tox_rate_at_dose_most_selected: (numeric)
observed toxicity rate at dose most often selected
n_obs: (ANY)
A numeric array (multi-dimensional) or list representing number of patients overall.
n_above_target: (integer)
number of patients treated above target tox interval
dose_grid: (numeric)
the dose grid that has been used
placebo: (logical)
set to TRUE (default is FALSE) for a design with placebo
any_backfilled: (flag)
indicates if any backfill cohorts were used
n_backfill: (ANY)
number of patients in backfill cohorts (only if any_backfilled=TRUE)
backfill_doses: (ANY)
list with doses used in backfill cohorts (only if any_backfilled=TRUE)

Note

Typically, end users will not use the .DefaultGeneralSimulationsSummary() function.

Get specific parameter samples and produce a data.frame

Description

Here you have to specify with pos which parameter you would like to extract from the Samples object

Usage

## S4 method for signature 'Samples,character'
get(x, pos = -1L, envir = NULL, mode = NULL, inherits = NULL)
## S4 method for signature 'Samples,character'
get(x, pos = -1L, envir = NULL, mode = NULL, inherits = NULL)

Arguments

x

the Samples object

pos

the name of the parameter

envir

for vectorial parameters, you can give the indices of the elements you would like to extract. If NULL, the whole vector samples will be returned

mode

not used

inherits

not used

Value

the data frame suitable for use with ggmcmc

Examples

# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# now extract the alpha0 samples (intercept of the regression model)
alpha0samples <- get(samples, "alpha0")

# nolint end
# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# now extract the alpha0 samples (intercept of the regression model)
alpha0samples <- get(samples, "alpha0")

# nolint end

Extracting Efficacy Responses for Subjects Categorized by the DLT

Description

A method that extracts efficacy responses for subjects and categorizes it with respect to DLT, i.e. DLT or no DLT. The efficacy responses are reported together with their corresponding dose levels.

Usage

getEff(object, ...)

## S4 method for signature 'DataDual'
getEff(object, no_dlt = FALSE)
getEff(object, ...)

## S4 method for signature 'DataDual'
getEff(object, no_dlt = FALSE)

Arguments

object

(DataDual)
object from which the responses and dose levels are extracted.

...

further arguments passed to class-specific methods.

no_dlt

(flag)
should only no DLT responses be returned? Otherwise, all responses are returned.

Value

list with efficacy responses categorized by the DLT value.

Examples

# Example data.
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25)
)

# Get the efficacy response and their corresponding dose levels
# categorized by the DLT.
getEff(data)
# Example data.
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25)
)

# Get the efficacy response and their corresponding dose levels
# categorized by the DLT.
getEff(data)

Comparison with Numerical Tolerance and Without Name Comparison

Description

This helper function ensures a default tolerance level equal to 1e-10, and ignores names and other attributes. In contrast to all.equal(), it always returns a logical type object.

Usage

h_all_equivalent(target, current, tolerance = 1e-10)
h_all_equivalent(target, current, tolerance = 1e-10)

Arguments

target

(numeric)
target values.

current

(numeric)
current values.

tolerance

(number) relative differences smaller than this are not reported.

Value

TRUE when target and current do not differ up to desired tolerance and without looking at names or other attributes, FALSE otherwise.

Helper Function to Blind Plot Data

Description

Helper Function to Blind Plot Data

Usage

h_blind_plot_data(df, blind, has_placebo, pbo_dose)
h_blind_plot_data(df, blind, has_placebo, pbo_dose)

Arguments

df

(GeneralData)
The data to be blinded

(flag)
Should the data be blinded?

has_placebo

(flag)
Does the data contain a placebo dose?

pbo_dose

(positive_number)
The dose to be taken as placebo. Ignored if has_placebo is FALSE

Value

The blinded data

Helper function to calculate percentage of true stopping rules for report label output calculates true column means and converts output into percentages before combining the output with the report label; output is passed to `show()` and output with cat to console

Description

Helper function to calculate percentage of true stopping rules for report label output calculates true column means and converts output into percentages before combining the output with the report label; output is passed to show() and output with cat to console

Usage

h_calc_report_label_percentage(stop_report)
h_calc_report_label_percentage(stop_report)

Arguments

stop_report

object from summary method

Value

named list with label and percentage of rule activation

Checking Formals of a Function

Description

This helper function checks whether a given function fun has required or allowed arguments. The argument check is based only on the names of the arguments. No any further logic is verified here.

Usage

h_check_fun_formals(fun, mandatory = NULL, allowed = NULL)
h_check_fun_formals(fun, mandatory = NULL, allowed = NULL)

Arguments

fun

(function)
a function name whose argument names will be checked.

mandatory

(character or NULL)
the names of the arguments which must be present in fun. If mandatory is specified as NULL (default) this requirement is ignored.

allowed

(character or NULL)
the names of the arguments which are allowed in fun. Names that do not belong to allowed are simply not allowed. The allowed parameter is independent from the mandatory, in a sense that if mandatory is specified as a character vector, it does not have to be repeated in allowed. If allowed is specified as NULL (default), then it means that there must be no any arguments in fun (except these ones which are specified in mandatory).

Convert a Ordinal Data to the Equivalent Binary Data for a Specific Grade

Description

A simple helper function that takes a DataOrdinal object and an integer grade and converts them to the equivalent Data object.

Usage

h_convert_ordinal_data(data_ord, grade)
h_convert_ordinal_data(data_ord, grade)

Arguments

data_ord

(DataOrdinal)
the DataOrdinal object to covert

grade

(integer)
the toxicity grade for which the equivalent data is required.

Value

A Data object.

Convert an ordinal CRM model to the Equivalent Binary CRM Model for a Specific Grade

Description

A simple helper function that takes a LogisticLogNormalOrdinal and an integer grade and converts them to the equivalent LogisticLogNormal model.

Usage

h_convert_ordinal_model(x, grade)
h_convert_ordinal_model(x, grade)

Arguments

x

(LogisticLogNormalOrdinal)
the LogisticLogNormalOrdinal model to covert

grade

(integer)
the toxicity grade for which the equivalent model is required.

Value

A LogisticLogNormal model.

Convert a Samples Object from an ordinal Model to the Equivalent Samples Object from a Binary Model

Description

A simple helper function that converts a Samples object from the fit of an ordinal CRM model to that which would have been obtained from fitting a binary CRM model for toxicities of a specified grade to the same observed data.

Usage

h_convert_ordinal_samples(obj, grade)
h_convert_ordinal_samples(obj, grade)

Arguments

obj

(Samples)
the Samples object to covert

grade

(integer)
the toxicity grade for which the equivalent data is required.

Value

A Samples object.

Getting the default value for an empty object

Description

A simple helper function that sets a default value for an empty or missing object, that is an object for which length() function returns 0L or it has length 1 and is.na() returns TRUE.

Usage

h_default_if_empty(x, default)
h_default_if_empty(x, default)

Arguments

x

(any)
an object to handle. It can be any object for which length() function is defined.

default

(any)
a default value for x object.

Examples

h_default_if_empty(character(0), default = "default label")
h_default_if_empty("custom label", default = "default label")
h_default_if_empty(NA, default = "default label")
h_default_if_empty(character(0), default = "default label")
h_default_if_empty("custom label", default = "default label")
h_default_if_empty(NA, default = "default label")

Find Interval Numbers or Indices and Return Custom Number For 0.

Description

A simple wrapper of findInterval() function that invokes findInterval(), takes its output and replaces all the elements with $0$ value to a custom number as specified in replacement argument.

Usage

h_find_interval(..., replacement = -Inf)
h_find_interval(..., replacement = -Inf)

Arguments

...

Arguments passed on to base::findInterval

x: numeric.
vec: numeric, sorted (weakly) increasingly, of length N, say.
rightmost.closed: logical; if true, the rightmost interval, vec[N-1] .. vec[N] is treated as closed, see below.
all.inside: logical; if true, the returned indices are coerced into 1,...,N-1, i.e., 0 is mapped to 1 and N to N-1.
left.open: logical; if true all the intervals are open at left and closed at right; in the formulas below, $\le$ should be swapped with $<$ (and $>$ with $\ge$ ), and rightmost.closed means ‘leftmost is closed’. This may be useful, e.g., in survival analysis computations.
checkSorted: logical indicating if vec should be checked, i.e., is.unsorted(vec) is asserted to be false. Setting this to FALSE skips the check gaining speed, but may return nonsense results in case vec is not sorted.
checkNA: logical indicating if each x[i] should be checked as with is.na(.). Setting this to FALSE in case of NA's in x[] may result in platform dependent nonsense.

replacement

(number)
a custom number to be used as a replacement for $0$ . Default to -Inf.

Examples

h_find_interval(1, c(2, 4, 6))
h_find_interval(3, c(2, 4, 6))
h_find_interval(1, c(2, 4, 6), replacement = -1)
h_find_interval(1, c(2, 4, 6))
h_find_interval(3, c(2, 4, 6))
h_find_interval(1, c(2, 4, 6), replacement = -1)

Conditional Formatting Using C-style Formats

Description

This helper function conditionally formats a number with formatC() function using "E" format and specific number of digits as given by the user. A number is formatted if and only if its absolute value is less than 0.001 or greater than 10000. Otherwise, the number is not formatted. Additionally, custom prefix or suffix can be appended to character string with formatted number, so that the changes are marked.

Usage

h_format_number(x, digits = 5, prefix = "", suffix = "")
h_format_number(x, digits = 5, prefix = "", suffix = "")

Arguments

x

(number)
a number to be formatted.

digits

(function)
the desired number of significant digits.

prefix

(string)
a prefix to be added in front of the formatted number.

suffix

(string)
a suffix to be appended after the formatted number.

Value

Either formatted x as string or unchanged x if the formatting condition is not met.

Note

This function was primarily designed as a helper for h_jags_write_model() function.

Examples

h_format_number(50000)
h_format_number(50000, prefix = "P", suffix = "S")
h_format_number(50000)
h_format_number(50000, prefix = "P", suffix = "S")

Check which elements are in a given range

Description

A simple helper function that tests whether elements of a given vector or matrix are within specified interval.

Usage

h_in_range(x, range = c(0, 1), bounds_closed = TRUE)
h_in_range(x, range = c(0, 1), bounds_closed = TRUE)

Arguments

x

(numeric)
vector or matrix with elements to test.

range

(numeric)
an interval, i.e. sorted two-elements vector.

bounds_closed

(logical)
should bounds in the range be treated as closed? This can be a scalar or vector of length two. If it is a scalar, then its value applies to lower bound range[1] and upper bound range[2]. If this is a vector with two flags, the first flag corresponds to the lower bound only, and the second to the upper bound only.

Value

A logical vector or matrix of length equal to the length of x, that for every element of x, indicates whether a given element of x is in the range.

Examples

x <- 1:4
h_in_range(x, range = c(1, 3))
h_in_range(x, range = c(1, 3), bounds_closed = FALSE)
h_in_range(x, range = c(1, 3), bounds_closed = c(FALSE, TRUE))
mat <- matrix(c(2, 5, 3, 10, 4, 9, 1, 8, 7), nrow = 3)
h_in_range(mat, range = c(1, 5))
x <- 1:4
h_in_range(x, range = c(1, 3))
h_in_range(x, range = c(1, 3), bounds_closed = FALSE)
h_in_range(x, range = c(1, 3), bounds_closed = c(FALSE, TRUE))
mat <- matrix(c(2, 5, 3, 10, 4, 9, 1, 8, 7), nrow = 3)
h_in_range(mat, range = c(1, 5))

Calculating the Information Theoretic Distance

Description

Helper function which provides the value of the divergence as given by equation in (7) in the reference at https://doi.org/10.1002/sim.8450.

Usage

h_info_theory_dist(prob, target, asymmetry)
h_info_theory_dist(prob, target, asymmetry)

Arguments

prob

(numeric)
vector or matrix with probabilities of a DLT occurring.

target

(number )
single target probability of a DLT.

asymmetry

(number)
describes the rate of penalization for overly toxic does, range 0 to 2.

Examples

h_info_theory_dist(c(0.5, 0.2), 0.4, 1.2)
h_info_theory_dist(c(0.5, 0.2), 0.4, 1.2)

Testing Matrix for Positive Definiteness

Description

This helper function checks whether a given numerical matrix x is a positive-definite square matrix of a given size, without any missing values. This function is used to test if a given matrix is a covariance matrix, since every symmetric positive semi-definite matrix is a covariance matrix.

Usage

h_is_positive_definite(x, size = 2, tol = 1e-06)
h_is_positive_definite(x, size = 2, tol = 1e-06)

Arguments

x

(matrix)
a matrix to be checked.

size

(integer)
a size of the square matrix x to be checked against for.

tol

(number)
a given tolerance number used to check whether an eigenvalue is positive or not. An eigenvalue is considered as positive if and only if it is greater than the tol.

Details

The positive definiteness test implemented in this function is based on the following characterization valid for real matrices: ⁠A symmetric matrix is positive-definite if and only if all of its eigenvalues are positive.⁠ In this function an eigenvalue is considered as positive if and only if it is greater than the tol.

Value

TRUE if a given matrix is a positive-definite, FALSE otherwise.

Appending a Dummy Number for Selected Slots in Data

Description

A helper function that appends a dummy value to a given slots in GeneralData class object, if and only if the total number of observations (as indicated by object@nObs) equals to 1. Otherwise, the object is not changed.

Usage

h_jags_add_dummy(object, where, dummy = 0)
h_jags_add_dummy(object, where, dummy = 0)

Arguments

object

(GeneralData)
object into which dummy values will be added.

where

(character)
names of slots in object to which a dummy number will be appended.

dummy

(number)
a dummy number that will be appended to selected slots in object. Default to 0.

Value

A GeneralData object with slots updated with dummy number.

Note

The main motivation behind this function is related to the JAGS. If there is only one observation, the data is not passed correctly to JAGS, i.e. e.g. x and y are treated like scalars in the data file. Therefore it is necessary to add dummy values to the vectors in this case As we don't change the number of observations (nObs), this addition of zeros doesn't affect the results of JAGS computations.

Examples

# Create some data of class 'Data'
my_data <- Data(
  x = 0.1,
  y = 0,
  doseGrid = c(0.1, 0.5)
)

my_data_2 <- Data(
  x = c(0.1, 0.5),
  y = c(0, 1),
  doseGrid = c(0.1, 0.5)
)

# Append dummy to `x` and `y`.
h_jags_add_dummy(my_data, where = c("x", "y"))

# Append dummy to `x` and `y`. No effect as `my_data_2@nObs != 1`.
h_jags_add_dummy(my_data_2, where = c("x", "y"))
# Create some data of class 'Data'
my_data <- Data(
  x = 0.1,
  y = 0,
  doseGrid = c(0.1, 0.5)
)

my_data_2 <- Data(
  x = c(0.1, 0.5),
  y = c(0, 1),
  doseGrid = c(0.1, 0.5)
)

# Append dummy to `x` and `y`.
h_jags_add_dummy(my_data, where = c("x", "y"))

# Append dummy to `x` and `y`. No effect as `my_data_2@nObs != 1`.
h_jags_add_dummy(my_data_2, where = c("x", "y"))

Extracting Samples from `JAGS` `mcarray` Object

Description

A simple helper function that extracts a sample from rjags::mcarray.object S3 class object. The rjags::mcarray.object object is used by the rjags::jags.samples() function to represent MCMC output from a JAGS model.

Usage

h_jags_extract_samples(x)
h_jags_extract_samples(x)

Arguments

x

an rjags::mcarray.object object.

Getting Data for `JAGS`

Description

A simple helper function that prepares an object for data argument of rjags::jags.model(), which is invoked by mcmc() method.

Usage

h_jags_get_data(model, data, from_prior)
h_jags_get_data(model, data, from_prior)

Arguments

model

(GeneralModel)
an input model.

data

(GeneralData)
an input data.

from_prior

(flag)
sample from the prior only? In this case data will not be appended to the output, i.e. only the variables required by the model@priormodel model will be returned in data.

Examples

# Create some data from the class `Data`.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

jags_data <- h_jags_get_data(my_model, my_data, from_prior = FALSE)
jags_data
# Create some data from the class `Data`.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

jags_data <- h_jags_get_data(my_model, my_data, from_prior = FALSE)
jags_data

Setting Initial Values for `JAGS` Model Parameters

Description

A simple helper function that prepares an object for inits argument of rjags::jags.model(), which is invoked by mcmc() method. The inits argument specifies initial values for model parameters.

Usage

h_jags_get_model_inits(model, data)
h_jags_get_model_inits(model, data)

Arguments

model

(GeneralModel)
an input model.

data

(GeneralData)
an input data.

Value

A list of starting values for parameters required to be initialized in the MCMC JAGS sampler.

Examples

# Create some data from the class `Data`.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

h_jags_get_model_inits(model = my_model, data = my_data)
# Create some data from the class `Data`.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

h_jags_get_model_inits(model = my_model, data = my_data)

Joining `JAGS` Models

Description

This helper function joins two JAGS models in the way that the body of the second model is appended to the body of the first model (in this order). After that, the first, body-extended model is returned. The arguments of model1, model2 model functions (if any) are not combined in any way.

Usage

h_jags_join_models(model1, model2)
h_jags_join_models(model1, model2)

Arguments

model1

(function)
the first model to join.

model2

(function)
the second model to join.

Value

joined models.

Note

model1 and model2 functions must have a multi-expression body, i.e. braced expression(s). Environments or any attributes of the function bodies are not preserved in any way after joining.

Writing JAGS Model to a File

Description

This function converts a R function with JAGS model into a text and then writes it into a given file. During the "model into text" conversion, the format of numbers of which absolute value is less than 0.001 or greater than 10000 is changed. These numbers will be converted into scientific format with specified number of significant digits using formatC() function.

Usage

h_jags_write_model(model, file = NULL, digits = 5)
h_jags_write_model(model, file = NULL, digits = 5)

Arguments

model

(function)
function containing the JAGS model.

file

(string or NULL)
the name of the file (including the optional path) where the model will be saved. If NULL, the file will be created in a R_crmPack folder placed under temporary directory indicated by tempdir() function.

digits

(count)
a desired number of significant digits for for numbers used in JAGS input, see formatC().

Value

The name of the file where the model was saved.

Note

JAGS syntax allows truncation specification like ⁠dnorm(...) I(...)⁠, which is illegal in R. To overcome this incompatibility, use dummy operator ⁠\%_\%⁠ before I(...), i.e. ⁠dnorm(...) \%_\% I(...)⁠ in the model's code. This dummy operator ⁠\%_\%⁠ will be removed just before saving the JAGS code into a file. Due to technical issues related to conversion of numbers to scientific format, it is required that the body of a model function does not contain TEMP_NUM_PREF_ or ⁠_TEMP_NUM_SUF⁠ character constants in its body.

Examples

# Some model function
my_model <- function() {
  alpha0 <- mean(1:10)
  alpha1 <- 600000
}

h_jags_write_model(my_model, digits = 5)
# Some model function
my_model <- function() {
  alpha0 <- mean(1:10)
  alpha1 <- 600000
}

h_jags_write_model(my_model, digits = 5)

Update certain components of `DualEndpoint` model with regard to parameters of the function that models dose-biomarker relationship defined in the `DualEndpointBeta` class.

Description

A simple helper function that takes DualEndpoint object and updates use_fixed, priormodel, modelspecs, init, sample slots with regard to a given parameter of the dose-biomarker relationship $f(x)$ defined in the DualEndpointBeta class. This update solely depends on whether a given parameter's value param is a fixed-valued scalar or two-elements numeric vector. In the later case, it is assumed that param represents two parameters of a probability distribution that will be used in priormodel function to generate values for the param_name parameter of $f(x)$ . See the help page for DualEndpointBeta class for more details.

Usage

h_model_dual_endpoint_beta(
  param,
  param_name,
  param_suffix = c("_low", "_high"),
  priormodel = NULL,
  de
)
h_model_dual_endpoint_beta(
  param,
  param_name,
  param_suffix = c("_low", "_high"),
  priormodel = NULL,
  de
)

Arguments

param

(numeric)
the value of a given param_name parameter of the dose-biomarker relationship function $f(x)$ . Either a fixed-valued scalar or vector with two elements that are the parameters of a probability distribution that will be used in priormodel function to generate values for the param_name parameter of $f(x)$ .

param_name

(string)
the name of the parameter of $f(x)$ , whose value depends on param.

param_suffix

(character)
the two suffixes to be appended to the elements of param_name and then used when updating modelspecs. The value of this argument is ignored when param is a scalar.

priormodel

(function or NULL)
a function representing the JAGS prior specification that will be appended to existing de@priormodel specification if param is not a scalar. Otherwise, de@priormodel remains unchanged.

de

(DualEnpoint)
dual endpoint model whose slots will be updated.

Value

A DualEndpoint model with updated use_fixed, priormodel, modelspecs, init, sample slots.

Update `DualEndpoint` class model components with regard to DLT and biomarker correlation.

Description

A simple helper function that takes DualEndpoint model existing components (priormodel, modelspecs, init, sample), and updates them with regard to DLT and biomarker correlation rho.

Usage

h_model_dual_endpoint_rho(use_fixed, rho, comp)
h_model_dual_endpoint_rho(use_fixed, rho, comp)

Arguments

use_fixed

(flag)
indicates whether a fixed value for DLT and biomarker correlation rho should be used or not. If rho is not supposed to be a fixed value, a prior distribution from the scaled Beta family will be used. See the details below, under rho argument.

rho

(numeric)
DLT and biomarker correlation. It must be either a fixed value (between -1 and 1), or a named vector with two elements, named a and b for the Beta prior on the transformation kappa = (rho + 1) / 2, which is in ⁠(0, 1)⁠. For example, ⁠a = 1, b = 1⁠ leads to a uniform prior on rho.

comp

(list)
a named list with model components that will be updated. The names should be: priormodel, modelspecs, init, sample. For definitions of the components, see GeneralModel class. The modelspecs and init components on comp list are specified up to the body of corresponding GeneralModel@modelspecs and GeneralModel@init functions. These bodies are simply a lists itself.

Value

A list with updated model components.

Update certain components of `DualEndpoint` model with regard to prior variance factor of the random walk.

Description

A simple helper function that takes DualEndpoint object and updates priormodel, modelspecs, init, sample slots according to the random walk variance.

Usage

h_model_dual_endpoint_sigma2betaw(use_fixed, sigma2betaW, de)
h_model_dual_endpoint_sigma2betaw(use_fixed, sigma2betaW, de)

Arguments

use_fixed

(flag)
indicates whether a fixed value for sigma2betaW should be used or not. If sigma2betaW is not supposed to be a fixed value, a prior distribution from the Inverse-Gamma distribution will be used. See the details below, under sigma2betaW argument.

sigma2betaW

(numeric)
the prior variance factor of the random walk prior for the biomarker model. Either a fixed value or Inverse-Gamma distribution parameters, i.e. vector with two elements named a and b.

de

(DualEnpoint)
dual endpoint model whose slots will be updated.

Value

A DualEndpoint model with updated priormodel, modelspecs, init, sample slots.

Update `DualEndpoint` class model components with regard to biomarker regression variance.

Description

A simple helper function that takes DualEndpoint model existing components (priormodel, modelspecs, init, sample), and updates them with regard to to biomarker regression variance sigma2W.

Usage

h_model_dual_endpoint_sigma2w(use_fixed, sigma2W, comp)
h_model_dual_endpoint_sigma2w(use_fixed, sigma2W, comp)

Arguments

use_fixed

(flag)
indicates whether a fixed value for the biomarker regression variance sigma2W should be used or not. If sigma2W is not supposed to be a fixed value, a prior distribution from the Inverse-Gamma distribution will be used. See the details below, under sigma2W argument.

sigma2W

(numeric)
the biomarker variance. Either a fixed value or Inverse-Gamma distribution parameters, i.e. vector with two elements named a and b.

comp

Value

list with updated model components.

Get Eligible Doses from the Dose Grid.

Description

Helper function that gets the eligible doses from the dose grid. The eligible doses are the doses which do not exceed a given doselimit. For placebo design, if safety allows (i.e. if there is at least one non-placebo dose which does not exceed the dose limit), the placebo dose is then excluded from the eligible doses.

Usage

h_next_best_eligible_doses(dose_grid, doselimit, placebo, levels = FALSE)
h_next_best_eligible_doses(dose_grid, doselimit, placebo, levels = FALSE)

Arguments

dose_grid

(numeric)
all possible doses.

doselimit

(number)
the maximum allowed next dose.

placebo

(flag)
if TRUE the first dose level in the dose_grid is considered as placebo.

levels

(flag)
if TRUE the levels of eligible doses are returned, otherwise, the doses (default).

Value

A numeric vector with eligible doses or eligible dose levels if levels flag is TRUE.

Examples

dose_grid <- c(0.001, seq(25, 200, 25))
h_next_best_eligible_doses(dose_grid, 79, TRUE)
h_next_best_eligible_doses(dose_grid, 24, TRUE)
dose_grid <- c(0.001, seq(25, 200, 25))
h_next_best_eligible_doses(dose_grid, 79, TRUE)
h_next_best_eligible_doses(dose_grid, 24, TRUE)

Credibility Intervals for Max Gain and Target Doses at `nextBest-NextBestMaxGain` Method.

Description

Helper function for nextBest-NextBestMaxGain() method. It computes a 95% credibility intervals for given target dose and max gain dose. It also returns a ratio of upper and lower bounds of the interval.

Usage

h_next_best_mg_ci(dose_target, dose_mg, prob_target, placebo, model, model_eff)
h_next_best_mg_ci(dose_target, dose_mg, prob_target, placebo, model, model_eff)

Arguments

dose_target

(number)
target dose estimate.

dose_mg

(number)
the dose corresponding to the maximum gain.

prob_target

(proportion)
target DLT probability.

placebo

(flag)
if TRUE the first dose level in the dose grid used is considered as placebo. This is needed to adjust the max gain dose using efficacy constant value. If the placebo was used, then the model_eff@const is added to dose_mg.

model

(ModelTox)
the DLT model.

model_eff

(Effloglog)
the efficacy model.

References

Get Closest Grid Doses for a Given Target Doses for `nextBest-NextBestMaxGain` Method.

Description

Helper function that for a given target doses finds the dose in grid that is closest and below the target. There are four different targets in the context of nextBest-NextBestMaxGain() method: $min(`dose_mg`, `dose_target_drt`)$ , dose_mg, dose_target_drt or dose_target_eot.

Usage

h_next_best_mg_doses_at_grid(
  dose_target_drt,
  dose_target_eot,
  dose_mg,
  dose_grid,
  doselimit,
  placebo
)
h_next_best_mg_doses_at_grid(
  dose_target_drt,
  dose_target_eot,
  dose_mg,
  dose_grid,
  doselimit,
  placebo
)

Arguments

dose_target_drt

(number)
target dose estimate during the trial.

dose_target_eot

(number)
target dose estimate at the end of the trial.

dose_mg

(number)
the dose corresponding to the maximum gain.

dose_grid

(numeric)
all possible doses.

doselimit

(number)
the maximum allowed next dose.

placebo

(flag)
if TRUE the first dose level in the dose_grid is considered as placebo.

Building the Plot for `nextBest-NextBestMaxGain` Method.

Description

Helper function which creates the plot for nextBest-NextBestMaxGain() method.

Usage

h_next_best_mg_plot(
  prob_target_drt,
  dose_target_drt,
  prob_target_eot,
  dose_target_eot,
  dose_mg,
  max_gain,
  next_dose,
  doselimit,
  data,
  model,
  model_eff
)
h_next_best_mg_plot(
  prob_target_drt,
  dose_target_drt,
  prob_target_eot,
  dose_target_eot,
  dose_mg,
  max_gain,
  next_dose,
  doselimit,
  data,
  model,
  model_eff
)

Arguments

prob_target_drt

(proportion)
target DLT probability during the trial.

dose_target_drt

(number)
target dose estimate during the trial.

prob_target_eot

(proportion)
target DLT probability at the end of the trial.

dose_target_eot

(number)
target dose estimate at the end of the trial.

dose_mg

(number)
the dose corresponding to the maximum gain.

max_gain

(number)
the maximum gain estimate.

next_dose

(number)
next best dose.

doselimit

(number)
the maximum allowed next dose.

data

(DataDual)
the data object from which the dose grid will be fetched.

model

(ModelTox)
the DLT model.

model_eff

(Effloglog)
the efficacy model.

Building the Plot for `nextBest-NextBestMaxGainSamples` Method.

Description

Helper function which creates the plot for nextBest-NextBestMaxGainSamples() method.

Usage

h_next_best_mgsamples_plot(
  prob_target_drt,
  dose_target_drt,
  prob_target_eot,
  dose_target_eot,
  dose_mg,
  dose_mg_samples,
  next_dose,
  doselimit,
  dose_grid_range
)
h_next_best_mgsamples_plot(
  prob_target_drt,
  dose_target_drt,
  prob_target_eot,
  dose_target_eot,
  dose_mg,
  dose_mg_samples,
  next_dose,
  doselimit,
  dose_grid_range
)

Arguments

prob_target_drt

(proportion)
target DLT probability during the trial.

dose_target_drt

(number)
target dose estimate during the trial.

prob_target_eot

(proportion)
target DLT probability at the end of the trial.

dose_target_eot

(number)
target dose estimate at the end of the trial.

dose_mg

(number)
the dose corresponding to the maximum gain.

dose_mg_samples

(numeric)
for every sample, the dose (from the dose grid) that gives the maximum gain value.

next_dose

(number)
next best dose.

doselimit

(number)
the maximum allowed next dose.

dose_grid_range

(numeric)
dose grid range.

Building the Plot for `nextBest-NextBestNCRMLoss` Method.

Description

Helper function which creates the plot for nextBest-NextBestNCRMLoss() method.

Usage

h_next_best_ncrm_loss_plot(
  prob_mat,
  posterior_loss,
  max_overdose_prob,
  dose_grid,
  max_eligible_dose_level,
  doselimit,
  next_dose,
  is_unacceptable_specified
)
h_next_best_ncrm_loss_plot(
  prob_mat,
  posterior_loss,
  max_overdose_prob,
  dose_grid,
  max_eligible_dose_level,
  doselimit,
  next_dose,
  is_unacceptable_specified
)

Arguments

prob_mat

(numeric)
matrix with probabilities of a grid doses to be in a given interval. If is_unacceptable_specified is TRUE, there must be 4 intervals (columns) in prob_mat: underdosing, target, excessive, unacceptable. Otherwise, there must be 3 intervals (columns): underdosing, target, overdose. Number of rows must be equal to number of doses in a grid.

posterior_loss

(numeric)
posterior losses.

max_overdose_prob

(number)
maximum overdose posterior probability that is allowed.

dose_grid

(numeric)
dose grid.

max_eligible_dose_level

(number)
maximum eligible dose level in the dose_grid.

doselimit

(number)
the maximum allowed next dose.

next_dose

(number)
next best dose.

is_unacceptable_specified

(flag)
is unacceptable interval specified?

Building the Plot for `nextBest-NextBestTD` Method.

Description

Helper function which creates the plot for nextBest-NextBestTD() method.

Usage

h_next_best_td_plot(
  prob_target_drt,
  dose_target_drt,
  prob_target_eot,
  dose_target_eot,
  data,
  prob_dlt,
  doselimit,
  next_dose
)
h_next_best_td_plot(
  prob_target_drt,
  dose_target_drt,
  prob_target_eot,
  dose_target_eot,
  data,
  prob_dlt,
  doselimit,
  next_dose
)

Arguments

prob_target_drt

(proportion)
target DLT probability during the trial.

dose_target_drt

(number)
target dose estimate during the trial.

prob_target_eot

(proportion)
target DLT probability at the end of the trial.

dose_target_eot

(number)
target dose estimate at the end of the trial.

data

(Data)
the data object from which the dose grid will be fetched.

prob_dlt

(numeric)
DLT probabilities for doses in grid.

doselimit

(number)
the maximum allowed next dose.

next_dose

(number)
next best dose.

Building the Plot for `nextBest-NextBestTDsamples` Method.

Description

Helper function which creates the plot for nextBest-NextBestTDsamples() method.

Usage

h_next_best_tdsamples_plot(
  dose_target_drt_samples,
  dose_target_eot_samples,
  dose_target_drt,
  dose_target_eot,
  dose_grid_range,
  nextBest,
  doselimit,
  next_dose
)
h_next_best_tdsamples_plot(
  dose_target_drt_samples,
  dose_target_eot_samples,
  dose_target_drt,
  dose_target_eot,
  dose_grid_range,
  nextBest,
  doselimit,
  next_dose
)

Arguments

dose_target_drt_samples

(numeric)
vector of in-trial samples.

dose_target_eot_samples

(numeric)
vector of end-of-trial samples.

dose_target_drt

(number)
target in-trial estimate.

dose_target_eot

(number)
target end-of-trial estimate.

dose_grid_range

(numeric)
range of dose grid.

nextBest

(NextBestTDsamples)
the rule for the next best dose.

doselimit

(number)
the maximum allowed next dose.

next_dose

(number)
next best dose.

Getting `NULL` for `NA`

Description

A simple helper function that replaces NA object by NULL object.

Usage

h_null_if_na(x)
h_null_if_na(x)

Arguments

x

(any)
atomic object of length 1. For the definition of "atomic", see is.atomic().

Value

NULL if x is NA, otherwise, x.

Examples

h_null_if_na(NA)
h_null_if_na(NA)

Helper Function Containing Common Functionality

Description

Used by dose_grid_range-Data and dose_grid_range-DataOrdinal

Usage

h_obtain_dose_grid_range(object, ignore_placebo)
h_obtain_dose_grid_range(object, ignore_placebo)

Arguments

object

(Data or DataOrdinal)
the object for which the dose grid range is required

ignore_placebo

(flag)
should placebo dose (if any) not be counted?

Preparing Cohort Lines for Data Plot

Description

This helper function prepares a ggplot geom with reference lines separating different cohorts on the plot of Data class object. Lines are either vertical or horizontal of green color and longdash type.

Usage

h_plot_data_cohort_lines(cohort, placebo, vertical = TRUE)
h_plot_data_cohort_lines(cohort, placebo, vertical = TRUE)

Arguments

cohort

(integer)
the cohort indices.

placebo

(flag)
is placebo included in the doses? If it so, this function returns NULL object as in this case all doses in a given cohort are equal and there is no need to separate them.

vertical

(flag)
should the line be vertical? Otherwise it is horizontal.

Details

The geom object is returned if and only if placebo is equal to TRUE and there are more than one unique values in cohort. Otherwise, this function returns NULL object.

Helper Function for the Plot Method of the Data and DataOrdinal Classes

Description

A method that creates a plot for Data and DataOrdinal objects.

A method that creates a plot for Data object.

A method that creates a plot for DataOrdinal object.

Usage

h_plot_data_dataordinal(
  x,
  blind = FALSE,
  legend = TRUE,
  tox_labels = c(Yes = "red", No = "black"),
  tox_shapes = c(Yes = 17L, No = 16L),
  mark_backfill = FALSE,
  ...
)

## S4 method for signature 'Data,missing'
plot(
  x,
  y,
  blind = FALSE,
  legend = TRUE,
  include_backfill = TRUE,
  mark_backfill = FALSE,
  mark_response = FALSE,
  ...,
  text_size_markings = 9,
  point_cex_markings = 1.1
)

## S4 method for signature 'DataOrdinal,missing'
plot(
  x,
  y,
  blind = FALSE,
  legend = TRUE,
  tox_labels = NULL,
  tox_shapes = NULL,
  ...
)
h_plot_data_dataordinal(
  x,
  blind = FALSE,
  legend = TRUE,
  tox_labels = c(Yes = "red", No = "black"),
  tox_shapes = c(Yes = 17L, No = 16L),
  mark_backfill = FALSE,
  ...
)

## S4 method for signature 'Data,missing'
plot(
  x,
  y,
  blind = FALSE,
  legend = TRUE,
  include_backfill = TRUE,
  mark_backfill = FALSE,
  mark_response = FALSE,
  ...,
  text_size_markings = 9,
  point_cex_markings = 1.1
)

## S4 method for signature 'DataOrdinal,missing'
plot(
  x,
  y,
  blind = FALSE,
  legend = TRUE,
  tox_labels = NULL,
  tox_shapes = NULL,
  ...
)

Arguments

x

(DataOrdinal)
object we want to plot.

(flag)
indicates whether to blind the data. If TRUE, then placebo subjects are reported at the same level as the active dose level in the corresponding cohort, and DLTs are always assigned to the first subjects in a cohort.

legend

(flag)
whether the legend should be added.

tox_labels

(⁠named list of character⁠)
the labels of the toxicity categories.

tox_shapes

(⁠names list of integers⁠)
the symbols used to identify the toxicity categories.

mark_backfill

(flag)
whether to mark backfilled patients with empty shape.

...

not used.

y

(missing)
missing object, for compatibility with the generic function.

include_backfill

(flag)
whether to include backfilled patients.

mark_response

(flag)
whether to mark patients with response with rectangle.

text_size_markings

(number)
text size for the markings legend.

point_cex_markings

(number)
point size multiplier for the markings legend.

Value

The ggplot2::ggplot2 object.

Note

The default values of tox_shapes and tox_labels result in DLTs being displayed as red triangles and other responses as black circles.

With more than 9 toxicity categories, toxicity symbols must be specified manually.
With more than 5 toxicity categories, toxicity labels must be specified manually.

Examples

# Create some data of class 'Data'.
my_data <- Data(
  x = c(0.001, 0.1, 0.1, 0.5, 0.001, 3, 3, 0.001, 10, 10, 10),
  y = c(0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0),
  cohort = c(1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4),
  doseGrid = c(0.001, 0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  placeb = TRUE
)

# Plot the data.
plot(my_data)

# We can also mark backfill patients and response outcomes.
my_data@backfilled <- c(
  FALSE,
  FALSE,
  FALSE,
  FALSE,
  TRUE,
  TRUE,
  TRUE,
  FALSE,
  FALSE,
  FALSE,
  FALSE
)
my_data@response <- as.integer(c(
  NA,
  NA,
  1,
  NA,
  NA,
  0,
  1,
  NA,
  1,
  0,
  0
))
plot(
  my_data,
  mark_backfill = TRUE,
  mark_response = TRUE
)
data <- DataOrdinal(
  x = c(10, 20, 30, 40, 50, 50, 50, 60, 60, 60),
  y = as.integer(c(0, 0, 0, 0, 0, 1, 0, 0, 1, 2)),
  ID = 1L:10L,
  cohort = as.integer(c(1:4, 5, 5, 5, 6, 6, 6)),
  doseGrid = c(seq(from = 10, to = 100, by = 10)),
  yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L),
  placebo = FALSE
)

plot(data)
# Create some data of class 'Data'.
my_data <- Data(
  x = c(0.001, 0.1, 0.1, 0.5, 0.001, 3, 3, 0.001, 10, 10, 10),
  y = c(0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0),
  cohort = c(1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4),
  doseGrid = c(0.001, 0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  placeb = TRUE
)

# Plot the data.
plot(my_data)

# We can also mark backfill patients and response outcomes.
my_data@backfilled <- c(
  FALSE,
  FALSE,
  FALSE,
  FALSE,
  TRUE,
  TRUE,
  TRUE,
  FALSE,
  FALSE,
  FALSE,
  FALSE
)
my_data@response <- as.integer(c(
  NA,
  NA,
  1,
  NA,
  NA,
  0,
  1,
  NA,
  1,
  0,
  0
))
plot(
  my_data,
  mark_backfill = TRUE,
  mark_response = TRUE
)
data <- DataOrdinal(
  x = c(10, 20, 30, 40, 50, 50, 50, 60, 60, 60),
  y = as.integer(c(0, 0, 0, 0, 0, 1, 0, 0, 1, 2)),
  ID = 1L:10L,
  cohort = as.integer(c(1:4, 5, 5, 5, 6, 6, 6)),
  doseGrid = c(seq(from = 10, to = 100, by = 10)),
  yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L),
  placebo = FALSE
)

plot(data)

Preparing Data for Plotting

Description

This helper function prepares a data.frame object based on Data class object. The resulting data frame is used by the plot function for Data class objects.

A method that transforms GeneralData objects into a tibble suitable for plotting with ggplot2 methods

Usage

h_plot_data_df(data, ...)

h_plot_data_df(data, ...)

## S4 method for signature 'Data'
h_plot_data_df(data, blind = FALSE, legend = TRUE, ...)

## S4 method for signature 'DataOrdinal'
h_plot_data_df(data, blind = FALSE, legend = TRUE, ...)
h_plot_data_df(data, ...)

h_plot_data_df(data, ...)

## S4 method for signature 'Data'
h_plot_data_df(data, blind = FALSE, legend = TRUE, ...)

## S4 method for signature 'DataOrdinal'
h_plot_data_df(data, blind = FALSE, legend = TRUE, ...)

Arguments

data

(Data)
object from which data is extracted and converted into a data frame.

...

further arguments passed to data.frame constructor. It can be e.g. an extra column_name = value pair based on a slot from x (which in this case might be a subclass of Data) which does not appear in Data.

(flag)
should data be blinded? If TRUE, then for each cohort, all DLTs are assigned to the first subjects in the cohort. In addition, the placebo (if any) is set to the active dose level for that cohort.

legend

(flag)
Display the legend for the toxicity categories

Value

A data.frame object with values to plot.

data.frame containing columns for patient, cohort, dose and toxicity grade

A data.frame object with columns patient, ID, cohort, dose and toxicity.

Methods (by class)

h_plot_data_df(Data): method for Data.
h_plot_data_df(DataOrdinal): Class specific method for DataOrdinal

Recursively Apply a Function to a List

Description

This helper function recursively iterates through a "list-like" object and it checks whether an element is of a given class. If it so, then it replaces that element by the result of an execution of a given function. Otherwise, and if the element is of length greater than 1 (i.e. not a scalar), it replaces that element by the result of h_rapply(), recursively called for that element. In the remaining case, that is, the element is not of a given class and is a scalar, then that element remains unchanged.

Usage

h_rapply(x, fun, classes, ...)
h_rapply(x, fun, classes, ...)

Arguments

x

(any)
"list-like" object for which subsetting operator [[ is defined.

fun

(function)
a function of one "principal" argument, passing further arguments via ....

classes

(character)
class names.

...

further arguments passed to function fun.

Value

"list-like" object of similar structure as x.

Note

This helper function is conceptually similar the same as rapply() function. However, it differs from rapply() in two major ways. First, the h_rapply() is not limited to objects of type list or expression only. It can be any "list-like" object of any type for which subsetting operator [[ is defined. This can be, for example, an object of type language, often obtained from the body() function. The second difference is that the flexibility of rapply() on how the result is structured is not available with h_rapply() for the user. That is, with h_rapply() each element of x, which has a class included in classes, is replaced by the result of applying fun to the element. This behavior corresponds to rapply() when invoked with fixed how = replace. This function was primarily designed as a helper for h_jags_write_model() function.

Examples

# Some model function.
my_model <- function() {
  alpha0 <- mean(1:10)
  alpha1 <- 600000
}

# Replace format of numbers using `formatC` function.
h_rapply(
  x = body(my_model),
  fun = formatC,
  classes = c("integer", "numeric"),
  digits = 3,
  format = "E"
)
# Some model function.
my_model <- function() {
  alpha0 <- mean(1:10)
  alpha1 <- 600000
}

# Replace format of numbers using `formatC` function.
h_rapply(
  x = body(my_model),
  fun = formatC,
  classes = c("integer", "numeric"),
  digits = 3,
  format = "E"
)

Getting the Slots from a S4 Object

Description

This helper function extracts requested slots from the S4 class object. It is a simple wrapper of methods::slot() function.

Usage

h_slots(object, names, simplify = FALSE)
h_slots(object, names, simplify = FALSE)

Arguments

object

(S4)
an object from a formally defined S4 class.

names

(character)
a vector with names of slots to be fetched. This function assumes that for every element in names, there exists a slot of the same name in the object.

simplify

(flag)
should an output be simplified? This has an effect if and only if a single slot is about to be extracted, i.e. names is just a single string.

Value

list with the slots extracted from object according to names, or single slot if simplification is required and possible.

Helper function to calculate average across iterations for each additional reporting parameter extracts parameter names as specified by user and averaged the values for each specified parameter to `show()` and output with cat to console

Description

Helper function to calculate average across iterations for each additional reporting parameter extracts parameter names as specified by user and averaged the values for each specified parameter to show() and output with cat to console

Usage

h_summarize_add_stats(stats_list)
h_summarize_add_stats(stats_list)

Arguments

stats_list

object from simulation with nested parameter values (sublist for each parameter)

Value

list of parameter names and averaged values for console output

Check that an argument is a named vector of type numeric

Description

A simple helper function that tests whether an object is a named numerical vector.

Usage

h_test_named_numeric(
  x,
  subset.of = NULL,
  must.include = NULL,
  permutation.of = NULL,
  identical.to = NULL,
  disjunct.from = NULL,
  lower = 0 + .Machine$double.xmin,
  finite = TRUE,
  any.missing = FALSE,
  len = 2,
  ...
)
h_test_named_numeric(
  x,
  subset.of = NULL,
  must.include = NULL,
  permutation.of = NULL,
  identical.to = NULL,
  disjunct.from = NULL,
  lower = 0 + .Machine$double.xmin,
  finite = TRUE,
  any.missing = FALSE,
  len = 2,
  ...
)

Arguments

x

(any)
object to check.

subset.of

[character]
Names provided in x must be subset of the set subset.of.

must.include

[character]
Names provided in x must be a superset of the set must.include.

permutation.of

[character]
Names provided in x must be a permutation of the set permutation.of. Duplicated names in permutation.of are stripped out and duplicated names in x thus lead to a failed check. Use this argument instead of identical.to if the order of the names is not relevant.

identical.to

[character]
Names provided in x must be identical to the vector identical.to. Use this argument instead of permutation.of if the order of the names is relevant.

disjunct.from

[character]
Names provided in x must may not be present in the vector disjunct.from.

lower

[numeric(1)]
Lower value all elements of x must be greater than or equal to.

finite

[logical(1)]
Check for only finite values? Default is FALSE.

any.missing

[logical(1)]
Are vectors with missing values allowed? Default is TRUE.

len

[integer(1)]
Exact expected length of x.

...

further parameters passed to checkmate::test_numeric().

Value

TRUE if x is a named vector of type numeric, otherwise FALSE.

Note

This function is based on checkmate::test_numeric() and checkmate::test_names() functions.

Examples

h_test_named_numeric(1:2, permutation.of = c("a", "b"))
h_test_named_numeric(c(a = 1, b = 2), permutation.of = c("a", "b"))
h_test_named_numeric(c(a = 1, b = 2), permutation.of = c("b", "a"))
h_test_named_numeric(1:2, permutation.of = c("a", "b"))
h_test_named_numeric(c(a = 1, b = 2), permutation.of = c("a", "b"))
h_test_named_numeric(c(a = 1, b = 2), permutation.of = c("b", "a"))

Helper function to recursively unpack stopping rules and return lists with logical value and label given

Description

Helper function to recursively unpack stopping rules and return lists with logical value and label given

Usage

h_unpack_stopit(stopit_tree)
h_unpack_stopit(stopit_tree)

Arguments

stopit_tree

object from simulate method

Value

named list

Combining S4 Class Validation Results

Description

A simple helper function that combines two outputs from calls to result() function which is placed in a slot of Validate() reference class.

Usage

h_validate_combine_results(v1, v2)
h_validate_combine_results(v1, v2)

Arguments

v1

(logical or character)
an output from result() function from Validate() reference class, to be combined with v2.

v2

(logical or character)
an output from result() function from Validate() reference class, to be combined with v1.

Examples

h_validate_combine_results(TRUE, "some_message")
h_validate_combine_results(TRUE, "some_message")

Helper Function performing validation Common to Data and DataOrdinal

Description

Helper Function performing validation Common to Data and DataOrdinal

Usage

h_validate_common_data_slots(object)
h_validate_common_data_slots(object)

Arguments

object

(Data or DataOrdinal)
the object to be validated

Value

a Validate object containing the result of the validation

`Increments`

Description

Increments is a virtual class for controlling increments, from which all other specific increments classes inherit.

Usage

.DefaultIncrements()
.DefaultIncrements()

Note

Typically, end users will not use the .DefaultIncrements() function.

`IncrementsDoseLevels`

Description

IncrementsDoseLevels is the class for increments control based on the number of dose levels.

Usage

IncrementsDoseLevels(levels = 1L, basis_level = "last")

.DefaultIncrementsDoseLevels()
IncrementsDoseLevels(levels = 1L, basis_level = "last")

.DefaultIncrementsDoseLevels()

Arguments

levels

(count)
see slot definition.

basis_level

(string)
see slot definition.

Slots

levels: (count)
maximum number of dose levels to increment for the next dose. It defaults to 1, which means that no dose skipping is allowed, i.e. the next dose can be maximum one level higher than the current base dose. The current base dose level is the dose level used to increment from (see basis_level parameter).
basis_level: (string)
defines the current base dose level. It can take one out of two possible values: last or max. If last is specified (default), the current base dose level is set to the last dose given. If max is specified, then the current base dose level is set to the maximum dose level given.

Note

Typically, end users will not use the .DefaultIncrementsDoseLevels() function.

Examples

# The rule for dose increments which allows for maximum skip one dose level,
# that is 2 dose levels higher than the last dose given.
my_increments <- IncrementsDoseLevels(levels = 2, basis_level = "last")
# The rule for dose increments which allows for maximum skip one dose level,
# that is 2 dose levels higher than the last dose given.
my_increments <- IncrementsDoseLevels(levels = 2, basis_level = "last")

`IncrementsHSRBeta`

Description

IncrementsHSRBeta is a class for limiting further increments using a Hard Safety Rule based on the Bin-Beta model. Increment control is based on the number of observed DLTs and number of subjects at each dose level. The probability of toxicity is calculated using a Bin-Beta model with prior (a,b). If the probability exceeds the threshold for a given dose, that dose and all doses above are excluded from further escalation. This is a hard safety rule that limits further escalation based on the observed data per dose level, independent from the underlying model.

Usage

IncrementsHSRBeta(target = 0.3, prob = 0.95, a = 1, b = 1)

.DefaultIncrementsHSRBeta()
IncrementsHSRBeta(target = 0.3, prob = 0.95, a = 1, b = 1)

.DefaultIncrementsHSRBeta()

Arguments

target

(proportion)
see slot definition.

prob

(proportion)
see slot definition.

a

(number)
see slot definition.

b

(number)
see slot definition.

Slots

target: (proportion)
the target toxicity, except 0 or 1.
prob: (proportion)
the threshold probability (except 0 or 1) for a dose being toxic.
a: (number)
shape parameter $a > 0$ of probability distribution Beta (a,b).
b: (number)
shape parameter $b > 0$ of probability distribution Beta (a,b).

Note

Typically, end users will not use the .DefaultIncrementsHSRBeta() function.

Examples

# Limit the escalation with a hard safety criteria to the doses that are below
# the first dose that is toxic with a probability of 0.95.
my_increments <- IncrementsHSRBeta(target = 0.3, prob = 0.95)
# Limit the escalation with a hard safety criteria to the doses that are below
# the first dose that is toxic with a probability of 0.95.
my_increments <- IncrementsHSRBeta(target = 0.3, prob = 0.95)

`IncrementsMaxToxProb`

Description

IncrementsMaxToxProb is the class for increments control based on probability of toxicity

Usage

IncrementsMaxToxProb(prob)

.DefaultIncrementsMaxToxProb()
IncrementsMaxToxProb(prob)

.DefaultIncrementsMaxToxProb()

Arguments

prob

(numeric)
see slot definition.

Slots

prob: (numeric)
See Usage Notes below.

Usage Notes

For binary models, prob should be a scalar probability.

For ordinal models, prob should be a named vector containing the maximum permissible probability of toxicity by grade. The names should match the names of the yCategories slot of the associated DataOrdinal object.

Note

Typically, end users will not use the .DefaultIncrementsMaxToxProb() function.

Examples

# For use with binary models and data
IncrementsMaxToxProb(prob = 0.35)

# For use with ordinal models and data
IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "DLT" = 0.05))
# For use with binary models and data
IncrementsMaxToxProb(prob = 0.35)

# For use with ordinal models and data
IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "DLT" = 0.05))

`IncrementsMin`

Description

IncrementsMin is the class that combines multiple increment rules with the minimum operation. Slot increments_list contains all increment rules, which are itself the objects of class Increments. The minimum of these individual increments is taken to give the final maximum increment.

Usage

IncrementsMin(increments_list)

.DefaultIncrementsMin()
IncrementsMin(increments_list)

.DefaultIncrementsMin()

Arguments

increments_list

(list)
see slot definition.

Slots

increments_list: (list)
list with increment rules.

Note

Typically, end users will not use the .DefaultIncrementsMin() function.

Examples

# As example, here we are combining 2 different increment rules.

# The first rule is the following:
# maximum doubling the dose if no DLTs were observed at the current dose
# or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
# or maximum increasing the dose by 1.22 if 3 or more DLTs were observed.
my_increments_1 <- IncrementsRelativeDLT(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)

# The second rule is the following:
# maximum doubling the dose if the current dose is <20
# or only maximum increasing the dose by 1.33 if the current dose is >=20.
my_increments_2 <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Now we combine the 2 rules.
comb_increments <- IncrementsMin(
  increments_list = list(my_increments_1, my_increments_2)
)
# As example, here we are combining 2 different increment rules.

# The first rule is the following:
# maximum doubling the dose if no DLTs were observed at the current dose
# or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
# or maximum increasing the dose by 1.22 if 3 or more DLTs were observed.
my_increments_1 <- IncrementsRelativeDLT(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)

# The second rule is the following:
# maximum doubling the dose if the current dose is <20
# or only maximum increasing the dose by 1.33 if the current dose is >=20.
my_increments_2 <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Now we combine the 2 rules.
comb_increments <- IncrementsMin(
  increments_list = list(my_increments_1, my_increments_2)
)

`IncrementsOrdinal`

Description

IncrementsOrdinal is the class for applying a standard Increments rule to the results of an ordinal CRM trial.

Usage

IncrementsOrdinal(grade, rule)

.DefaultIncrementsOrdinal()
IncrementsOrdinal(grade, rule)

.DefaultIncrementsOrdinal()

Arguments

grade

(numeric)
see slot definition.

rule

(Increments)
see slot definition.

Slots

grade: (integer)
the toxicity grade to which the rule should be applied.
rule: (Increments)
the standard Increments rule to be applied

Note

Typically, end users will not use the .DefaultIncrementsOrdinal() function.

Examples

IncrementsOrdinal(
  grade = 1L,
  rule = IncrementsRelative(
    intervals = c(0, 20),
    increments = c(1, 0.33)
  )
)
IncrementsOrdinal(
  grade = 1L,
  rule = IncrementsRelative(
    intervals = c(0, 20),
    increments = c(1, 0.33)
  )
)

`IncrementsRelative`

Description

IncrementsRelative is the class for increments control based on relative differences in intervals.

Usage

IncrementsRelative(intervals, increments)

.DefaultIncrementsRelative()
IncrementsRelative(intervals, increments)

.DefaultIncrementsRelative()

Arguments

intervals

(numeric)
see slot definition.

increments

(numeric)
see slot definition.

Slots

intervals: (numeric)
a vector with the left bounds of the relevant intervals. For example, intervals = c(0, 50, 100) specifies three intervals: $(0, 50)$ , $[50, 100)$ and $[100, +Inf)$ . That means, the right bound of the intervals are exclusive to the interval and the last interval goes from the last value to infinity.
increments: (numeric)
a vector of the same length with the maximum allowable relative increments in the intervals.

Note

Typically, end users will not use the .DefaultIncrementsRelative() function.

Examples

# This is the example of a rule for:
# maximum doubling the dose if the current dose is <20
# or only maximum increasing the dose by 1.33 if the current dose is >=20.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
# This is the example of a rule for:
# maximum doubling the dose if the current dose is <20
# or only maximum increasing the dose by 1.33 if the current dose is >=20.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

`IncrementsRelativeDLT`

Description

IncrementsRelativeDLT is the class for increments control based on relative differences in terms of DLTs.

Usage

IncrementsRelativeDLT(intervals, increments)

.DefaultIncrementsRelativeDLT()
IncrementsRelativeDLT(intervals, increments)

.DefaultIncrementsRelativeDLT()

Arguments

intervals

(numeric)
see slot definition.

increments

(numeric)
see slot definition.

Slots

intervals: (integer)
a vector with the left bounds of the relevant DLT intervals. For example, intervals = c(0, 1, 3) specifies three intervals (sets of DLTs: first, 0 DLT; second 1 or 2 DLTs; and the third one, at least 3 DLTs. That means, the right bound of the intervals are exclusive to the interval and the last interval goes from the last value to infinity.
increments: (numeric)
a vector of maximum allowable relative increments corresponding to intervals. IT must be of the same length as the length of intervals.

Note

This considers all DLTs across all cohorts observed so far.

Typically, end users will not use the .DefaultIncrementsRelativeDLT() function.

Examples

# This is the example of a rule for:
# maximum doubling the dose if no DLTs were observed in the whole study so far
# or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed so far
# or maximum increasing the dose by 1.22 if 3 or more DLTs were observed so far.
my_increments <- IncrementsRelativeDLT(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)
# This is the example of a rule for:
# maximum doubling the dose if no DLTs were observed in the whole study so far
# or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed so far
# or maximum increasing the dose by 1.22 if 3 or more DLTs were observed so far.
my_increments <- IncrementsRelativeDLT(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)

`IncrementsRelativeDLTCurrent`

Description

IncrementsRelativeDLTCurrent is the class for increments control based on relative differences and current DLTs. The class is based on the number of DLTs observed in the current cohort, but not cumulatively over all cohorts so far.

Usage

IncrementsRelativeDLTCurrent(intervals = c(0L, 1L), increments = c(2L, 1L))

.DefaultIncrementsRelativeDLTCurrent()
IncrementsRelativeDLTCurrent(intervals = c(0L, 1L), increments = c(2L, 1L))

.DefaultIncrementsRelativeDLTCurrent()

Arguments

intervals

(numeric)
see slot definition.

increments

(numeric)
see slot definition.

Note

Typically, end users will not use the .DefaultIncrementsRelativeDLTCurrent() function.

Examples

# As example, here is the rule for:
# maximum doubling the dose if no DLTs were observed at the current dose
# or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
# or maximum increasing the dose by 1.22 if 3 or more DLTs were observed.

my_increments <- IncrementsRelativeDLTCurrent(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)
# As example, here is the rule for:
# maximum doubling the dose if no DLTs were observed at the current dose
# or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
# or maximum increasing the dose by 1.22 if 3 or more DLTs were observed.

my_increments <- IncrementsRelativeDLTCurrent(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)

`IncrementsRelativeParts`

Description

IncrementsRelativeParts is the class for increments control based on relative differences in intervals, with special rules for part 1 and beginning of part 2.

Usage

IncrementsRelativeParts(dlt_start, clean_start, ...)

.DefaultIncrementsRelativeParts()
IncrementsRelativeParts(dlt_start, clean_start, ...)

.DefaultIncrementsRelativeParts()

Arguments

dlt_start

(count)
see slot definition.

clean_start

(count)
see slot definition.

...

Arguments passed on to IncrementsRelative

intervals: (numeric)
see slot definition.
increments: (numeric)
see slot definition.

Details

This class works only in conjunction with DataParts objects. If part 2 will just be started in the next cohort, then the next maximum dose will be either dlt_start (e.g. -1) shift of the last part 1 dose in case of a DLT in part 1, or clean_start shift (e.g. -1) in case of no DLTs in part 1, given that clean_start <= 0 (see description of clean_start slot for more details). If part 1 will still be on in the next cohort, then the next dose level will be the next higher dose level in the part1Ladder slot of the data object. If part 2 has been started before, the usual relative increment rules apply, see IncrementsRelative.

Slots

dlt_start: (integer)
a scalar, the dose level increment for starting part 2 in case of at least one DLT event in part 1.
clean_start: (integer)
a scalar, the dose level increment for starting part 2 in case of no DLTs in part 1. If clean_start <= 0, then the part 1 ladder will be used to find the maximum next dose. Otherwise, the relative increment rules will be applied to find the next maximum dose level.

Note

We require that clean_start >= dlt_start. However, this precondition is not a prerequisite for any function (except of the class' validation function) that works with objects of this class. It is rather motivated by the semantics. That is, if we observe a DLT in part 1, we cannot be more aggressive than in case of a clean part 1 without DLT.

Typically, end users will not use the .DefaultIncrementsRelativeParts() function.

Examples

my_increments <- IncrementsRelativeParts(dlt_start = 0, clean_start = 1)
my_increments <- IncrementsRelativeParts(dlt_start = 0, clean_start = 1)

Render a `CohortSizeConst` Object

Description

We provide additional utility functions to allow human-friendly rendition of crmPack objects in Markdown and Quarto files

We provide additional utility functions to allow human-friendly rendition of crmPack objects in Markdown and Quarto files. This file contains methods for all design classes, not just those that are direct descendants of Design.

We provide additional utility functions to allow human-friendly rendition of crmPack objects in Markdown and Quarto files

Usage

## S3 method for class 'Backfill'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'CohortSizeConst'
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))

## S3 method for class 'CohortSizeRange'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'CohortSizeDLT'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'CohortSizeParts'
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))

## S3 method for class 'CohortSizeMax'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'CohortSizeMin'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'CohortSizeOrdinal'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'CohortSizeRandom'
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))

## S3 method for class 'StartingDose'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'RuleDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'Design'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DualDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DADesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'TDDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DualResponsesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DesignOrdinal'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DesignGrouped'
knit_print(
  x,
  ...,
  level = 2L,
  title = "Design",
  sections = c(model = "Dose toxicity model", mono = "Monotherapy rules", combo =
    "Combination therapy rules", other = "Other details"),
  asis = TRUE
)

## S3 method for class 'TDsamplesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DualResponsesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DualResponsesSamplesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'RuleDesignOrdinal'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'GeneralData'
knit_print(
  x,
  ...,
  asis = TRUE,
  label = c("participant", "participants"),
  full_grid = FALSE,
  summarise = c("none", "dose", "cohort"),
  summarize = summarise,
  units = NA,
  format_func = h_knit_format_func
)

## S3 method for class 'DataParts'
knit_print(
  x,
  ...,
  asis = TRUE,
  label = c("participant", "participants"),
  full_grid = FALSE,
  summarise = c("none", "dose", "cohort"),
  summarize = summarise,
  units = NA,
  format_func = h_knit_format_func
)

## S3 method for class 'DualEndpoint'
knit_print(
  x,
  ...,
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  units = NA,
  tox_label = "toxicity",
  biomarker_label = "PD biomarker"
)

## S3 method for class 'ModelParamsNormal'
knit_print(
  x,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c("alpha", "beta"),
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE,
  theta = "\\theta",
  ...
)

## S3 method for class 'GeneralModel'
knit_print(
  x,
  ...,
  params = c("alpha", "beta"),
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  units = NA
)

## S3 method for class 'LogisticKadane'
knit_print(
  x,
  ...,
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  units = NA,
  tox_label = "toxicity"
)

## S3 method for class 'LogisticKadaneBetaGamma'
knit_print(
  x,
  ...,
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  tox_label = "toxicity",
  units = NA
)

## S3 method for class 'LogisticLogNormal'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c(`\\alpha` = "alpha", `log(\\beta)` = "beta"),
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'LogisticLogNormalMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)

## S3 method for class 'LogisticLogNormalSub'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c(`\\alpha` = "alpha", `log(\\beta)` = "beta"),
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'LogisticNormalMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)

## S3 method for class 'LogisticNormalFixedMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)

## S3 method for class 'OneParLogNormalPrior'
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f"
)

## S3 method for class 'OneParExpPrior'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'LogisticLogNormalGrouped'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c(`\\alpha` = "alpha", `\\beta` = "beta", `log(\\delta_0)` = "delta_0",
    `log(\\delta_1)` = "delta_1"),
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'LogisticLogNormalOrdinal'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = NA,
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'LogisticIndepBeta'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = NA,
  tox_label = "DLAE",
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'Effloglog'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = NA,
  tox_label = "DLAE",
  eff_label = "efficacy",
  label = "participant",
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'IncrementsRelative'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsRelativeDLT'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsDoseLevels'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsHSRBeta'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsMin'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsOrdinal'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsRelativeParts'
knit_print(x, ..., asis = TRUE, tox_label = c("toxicity", "toxicities"))

## S3 method for class 'IncrementsRelativeDLTCurrent'
knit_print(x, ..., asis = TRUE, tox_label = c("DLT", "DLTs"))

## S3 method for class 'NextBestMTD'
knit_print(
  x,
  ...,
  target_label = "the 25th centile",
  tox_label = "toxicity",
  asis = TRUE
)

## S3 method for class 'NextBestNCRM'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestThreePlusThree'
knit_print(
  x,
  ...,
  tox_label = c("toxicity", "toxicities"),
  label = "participant",
  asis = TRUE
)

## S3 method for class 'NextBestDualEndpoint'
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  biomarker_label = "the biomarker",
  biomarker_units = ifelse(x@target_relative, "%", ""),
  asis = TRUE
)

## S3 method for class 'NextBestMinDist'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestInfTheory'
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  citation_text = "Mozgunov & Jaki (2019)",
  citation_link = "https://doi.org/10.1002/sim.8450",
  asis = TRUE
)

## S3 method for class 'NextBestTD'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestMaxGain'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestProbMTDLTE'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestProbMTDMinDist'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestNCRMLoss'
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  asis = TRUE,
  format_func = h_knit_format_func
)

## S3 method for class 'NextBestTDsamples'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestMaxGainSamples'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestOrdinal'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'OpeningMinDose'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'OpeningMinCohorts'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'OpeningNone'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'OpeningMinResponses'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'OpeningList'
knit_print(x, ..., preamble, indent = 0L, asis = TRUE)

## S3 method for class 'OpeningAll'
knit_print(x, ..., preamble, asis = TRUE)

## S3 method for class 'OpeningAny'
knit_print(x, ..., preamble, asis = TRUE)

## S3 method for class 'RecruitmentUnlimited'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'RecruitmentRatio'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'SafetyWindow'
knit_print(x, ..., asis = TRUE, time_unit = "day", label = "participant")

## S3 method for class 'SafetyWindowConst'
knit_print(
  x,
  ...,
  asis = TRUE,
  label = "participant",
  ordinals = c("first", "second", "third", "fourth", "fifth", "sixth", "seventh",
    "eighth", "ninth", "tenth"),
  time_unit = "day"
)

## S3 method for class 'SafetyWindowSize'
knit_print(
  x,
  ...,
  asis = TRUE,
  ordinals = c("first", "second", "third", "fourth", "fifth", "sixth", "seventh",
    "eighth", "ninth", "tenth"),
  label = "participant",
  time_unit = "day",
  level = 2L
)

## S3 method for class 'StoppingOrdinal'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'StoppingMaxGainCIRatio'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'StoppingList'
knit_print(x, ..., preamble, indent = 0L, asis = TRUE)

## S3 method for class 'StoppingAny'
knit_print(x, ..., preamble, asis = TRUE)

## S3 method for class 'StoppingAll'
knit_print(x, ..., preamble, asis = TRUE)

## S3 method for class 'StoppingTDCIRatio'
knit_print(
  x,
  ...,
  dose_label = "the next best dose",
  tox_label = "toxicity",
  fmt_string =
    paste0("%sIf, at %s, the ratio of the upper to the lower limit of the posterior ",
    "95%% credible interval for %s (targetting %2.0f%%) is less than or equal to "),
  asis = TRUE
)

## S3 method for class 'StoppingTargetBiomarker'
knit_print(
  x,
  ...,
  dose_label = "the next best dose",
  biomarker_label = "the target biomarker",
  fmt_string =
    paste0("%sIf, at %s, the posterior probability that %s is in the range ",
    "(%.2f, %.2f)%s is %.0f%% or more.\n\n"),
  asis = TRUE
)

## S3 method for class 'StoppingLowestDoseHSRBeta'
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  fmt_string =
    paste0("%sIf, using a Hard Stopping Rule with a prior of Beta(%.0f, %.0f), the ",
    "lowest dose in the dose grid has a posterior probability of %s of ",
    "%.0f%% or more.\n\n"),
  asis = TRUE
)

## S3 method for class 'StoppingMTDCV'
knit_print(
  x,
  ...,
  fmt_string =
    paste0("%sIf the posterior estimate of the robust coefficient of variation of ",
    "the MTD (targetting %2.0f%%), is than or equal to %.0f%%.\n\n"),
  asis = TRUE
)

## S3 method for class 'StoppingMTDdistribution'
knit_print(
  x,
  ...,
  fmt_string =
    "%sIf the mean posterior probability of %s at %.0f%% of %s is at least %4.2f.\n\n",
  dose_label = "the next best dose",
  tox_label = "toxicity",
  asis = TRUE
)

## S3 method for class 'StoppingHighestDose'
knit_print(
  x,
  ...,
  dose_label = "the highest dose in the dose grid",
  asis = TRUE
)

## S3 method for class 'StoppingSpecificDose'
knit_print(x, ..., dose_label = as.character(x@dose), asis = TRUE)

## S3 method for class 'StoppingTargetProb'
knit_print(
  x,
  ...,
  fmt_string =
    paste0("%sIf the probability of %s at %s is in the range [%4.2f, %4.2f] ",
    "is at least %4.2f.\n\n"),
  dose_label = "the next best dose",
  tox_label = "toxicity",
  asis = TRUE
)

## S3 method for class 'StoppingMinCohorts'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'StoppingMinPatients'
knit_print(x, ..., label = "participant", asis = TRUE)

## S3 method for class 'StoppingPatientsNearDose'
knit_print(
  x,
  ...,
  dose_label = "the next best dose",
  label = "participants",
  asis = TRUE
)

## S3 method for class 'StoppingCohortsNearDose'
knit_print(x, ..., dose_label = "the next best dose", asis = TRUE)

## S3 method for class 'StoppingMissingDose'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'Backfill'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'CohortSizeConst'
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))

## S3 method for class 'CohortSizeRange'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'CohortSizeDLT'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'CohortSizeParts'
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))

## S3 method for class 'CohortSizeMax'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'CohortSizeMin'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'CohortSizeOrdinal'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'CohortSizeRandom'
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))

## S3 method for class 'StartingDose'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'RuleDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'Design'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DualDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DADesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'TDDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DualResponsesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DesignOrdinal'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DesignGrouped'
knit_print(
  x,
  ...,
  level = 2L,
  title = "Design",
  sections = c(model = "Dose toxicity model", mono = "Monotherapy rules", combo =
    "Combination therapy rules", other = "Other details"),
  asis = TRUE
)

## S3 method for class 'TDsamplesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DualResponsesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'DualResponsesSamplesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'RuleDesignOrdinal'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)

## S3 method for class 'GeneralData'
knit_print(
  x,
  ...,
  asis = TRUE,
  label = c("participant", "participants"),
  full_grid = FALSE,
  summarise = c("none", "dose", "cohort"),
  summarize = summarise,
  units = NA,
  format_func = h_knit_format_func
)

## S3 method for class 'DataParts'
knit_print(
  x,
  ...,
  asis = TRUE,
  label = c("participant", "participants"),
  full_grid = FALSE,
  summarise = c("none", "dose", "cohort"),
  summarize = summarise,
  units = NA,
  format_func = h_knit_format_func
)

## S3 method for class 'DualEndpoint'
knit_print(
  x,
  ...,
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  units = NA,
  tox_label = "toxicity",
  biomarker_label = "PD biomarker"
)

## S3 method for class 'ModelParamsNormal'
knit_print(
  x,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c("alpha", "beta"),
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE,
  theta = "\\theta",
  ...
)

## S3 method for class 'GeneralModel'
knit_print(
  x,
  ...,
  params = c("alpha", "beta"),
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  units = NA
)

## S3 method for class 'LogisticKadane'
knit_print(
  x,
  ...,
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  units = NA,
  tox_label = "toxicity"
)

## S3 method for class 'LogisticKadaneBetaGamma'
knit_print(
  x,
  ...,
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f",
  tox_label = "toxicity",
  units = NA
)

## S3 method for class 'LogisticLogNormal'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c(`\\alpha` = "alpha", `log(\\beta)` = "beta"),
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'LogisticLogNormalMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)

## S3 method for class 'LogisticLogNormalSub'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c(`\\alpha` = "alpha", `log(\\beta)` = "beta"),
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'LogisticNormalMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)

## S3 method for class 'LogisticNormalFixedMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)

## S3 method for class 'OneParLogNormalPrior'
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  asis = TRUE,
  use_values = TRUE,
  fmt = "%5.2f"
)

## S3 method for class 'OneParExpPrior'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'LogisticLogNormalGrouped'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = c(`\\alpha` = "alpha", `\\beta` = "beta", `log(\\delta_0)` = "delta_0",
    `log(\\delta_1)` = "delta_1"),
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'LogisticLogNormalOrdinal'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = NA,
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'LogisticIndepBeta'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = NA,
  tox_label = "DLAE",
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'Effloglog'
knit_print(
  x,
  ...,
  use_values = TRUE,
  fmt = "%5.2f",
  params = NA,
  tox_label = "DLAE",
  eff_label = "efficacy",
  label = "participant",
  preamble = "The prior for &theta; is given by\\n",
  asis = TRUE
)

## S3 method for class 'IncrementsRelative'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsRelativeDLT'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsDoseLevels'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsHSRBeta'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsMin'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsOrdinal'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'IncrementsRelativeParts'
knit_print(x, ..., asis = TRUE, tox_label = c("toxicity", "toxicities"))

## S3 method for class 'IncrementsRelativeDLTCurrent'
knit_print(x, ..., asis = TRUE, tox_label = c("DLT", "DLTs"))

## S3 method for class 'NextBestMTD'
knit_print(
  x,
  ...,
  target_label = "the 25th centile",
  tox_label = "toxicity",
  asis = TRUE
)

## S3 method for class 'NextBestNCRM'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestThreePlusThree'
knit_print(
  x,
  ...,
  tox_label = c("toxicity", "toxicities"),
  label = "participant",
  asis = TRUE
)

## S3 method for class 'NextBestDualEndpoint'
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  biomarker_label = "the biomarker",
  biomarker_units = ifelse(x@target_relative, "%", ""),
  asis = TRUE
)

## S3 method for class 'NextBestMinDist'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestInfTheory'
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  citation_text = "Mozgunov & Jaki (2019)",
  citation_link = "https://doi.org/10.1002/sim.8450",
  asis = TRUE
)

## S3 method for class 'NextBestTD'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestMaxGain'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestProbMTDLTE'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestProbMTDMinDist'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestNCRMLoss'
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  asis = TRUE,
  format_func = h_knit_format_func
)

## S3 method for class 'NextBestTDsamples'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestMaxGainSamples'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'NextBestOrdinal'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)

## S3 method for class 'OpeningMinDose'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'OpeningMinCohorts'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'OpeningNone'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'OpeningMinResponses'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'OpeningList'
knit_print(x, ..., preamble, indent = 0L, asis = TRUE)

## S3 method for class 'OpeningAll'
knit_print(x, ..., preamble, asis = TRUE)

## S3 method for class 'OpeningAny'
knit_print(x, ..., preamble, asis = TRUE)

## S3 method for class 'RecruitmentUnlimited'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'RecruitmentRatio'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'SafetyWindow'
knit_print(x, ..., asis = TRUE, time_unit = "day", label = "participant")

## S3 method for class 'SafetyWindowConst'
knit_print(
  x,
  ...,
  asis = TRUE,
  label = "participant",
  ordinals = c("first", "second", "third", "fourth", "fifth", "sixth", "seventh",
    "eighth", "ninth", "tenth"),
  time_unit = "day"
)

## S3 method for class 'SafetyWindowSize'
knit_print(
  x,
  ...,
  asis = TRUE,
  ordinals = c("first", "second", "third", "fourth", "fifth", "sixth", "seventh",
    "eighth", "ninth", "tenth"),
  label = "participant",
  time_unit = "day",
  level = 2L
)

## S3 method for class 'StoppingOrdinal'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'StoppingMaxGainCIRatio'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'StoppingList'
knit_print(x, ..., preamble, indent = 0L, asis = TRUE)

## S3 method for class 'StoppingAny'
knit_print(x, ..., preamble, asis = TRUE)

## S3 method for class 'StoppingAll'
knit_print(x, ..., preamble, asis = TRUE)

## S3 method for class 'StoppingTDCIRatio'
knit_print(
  x,
  ...,
  dose_label = "the next best dose",
  tox_label = "toxicity",
  fmt_string =
    paste0("%sIf, at %s, the ratio of the upper to the lower limit of the posterior ",
    "95%% credible interval for %s (targetting %2.0f%%) is less than or equal to "),
  asis = TRUE
)

## S3 method for class 'StoppingTargetBiomarker'
knit_print(
  x,
  ...,
  dose_label = "the next best dose",
  biomarker_label = "the target biomarker",
  fmt_string =
    paste0("%sIf, at %s, the posterior probability that %s is in the range ",
    "(%.2f, %.2f)%s is %.0f%% or more.\n\n"),
  asis = TRUE
)

## S3 method for class 'StoppingLowestDoseHSRBeta'
knit_print(
  x,
  ...,
  tox_label = "toxicity",
  fmt_string =
    paste0("%sIf, using a Hard Stopping Rule with a prior of Beta(%.0f, %.0f), the ",
    "lowest dose in the dose grid has a posterior probability of %s of ",
    "%.0f%% or more.\n\n"),
  asis = TRUE
)

## S3 method for class 'StoppingMTDCV'
knit_print(
  x,
  ...,
  fmt_string =
    paste0("%sIf the posterior estimate of the robust coefficient of variation of ",
    "the MTD (targetting %2.0f%%), is than or equal to %.0f%%.\n\n"),
  asis = TRUE
)

## S3 method for class 'StoppingMTDdistribution'
knit_print(
  x,
  ...,
  fmt_string =
    "%sIf the mean posterior probability of %s at %.0f%% of %s is at least %4.2f.\n\n",
  dose_label = "the next best dose",
  tox_label = "toxicity",
  asis = TRUE
)

## S3 method for class 'StoppingHighestDose'
knit_print(
  x,
  ...,
  dose_label = "the highest dose in the dose grid",
  asis = TRUE
)

## S3 method for class 'StoppingSpecificDose'
knit_print(x, ..., dose_label = as.character(x@dose), asis = TRUE)

## S3 method for class 'StoppingTargetProb'
knit_print(
  x,
  ...,
  fmt_string =
    paste0("%sIf the probability of %s at %s is in the range [%4.2f, %4.2f] ",
    "is at least %4.2f.\n\n"),
  dose_label = "the next best dose",
  tox_label = "toxicity",
  asis = TRUE
)

## S3 method for class 'StoppingMinCohorts'
knit_print(x, ..., asis = TRUE)

## S3 method for class 'StoppingMinPatients'
knit_print(x, ..., label = "participant", asis = TRUE)

## S3 method for class 'StoppingPatientsNearDose'
knit_print(
  x,
  ...,
  dose_label = "the next best dose",
  label = "participants",
  asis = TRUE
)

## S3 method for class 'StoppingCohortsNearDose'
knit_print(x, ..., dose_label = "the next best dose", asis = TRUE)

## S3 method for class 'StoppingMissingDose'
knit_print(x, ..., asis = TRUE)

Arguments

x

(ModelParamsNormal)
the object to be rendered

...

passed to knitr::kable()

asis

(flag)
Not used at present

label

(character)
the term used to label participants

tox_label

(character)
the term used to describe toxicity

level

(count)
the markdown level at which the headings for cohort size will be printed. An integer between 1 and 6

title

(character) The text of the heading of the section describing the design

sections

(character) a named vector of length at least 4 defining the headings used to define the sections corresponding to the design's slots. The element names must match the Design's slot names.

full_grid

(flag)
Should the full dose grid appear in the output table or simply those doses for whom at least one evaluable participant is available? Ignored unless summarise == "dose".

summarise

(character)
How to summarise the observed data. The default, "none", lists observed data at the participant level. "dose" presents participant counts by dose and "cohort" by cohort.

summarize

(character)
Synonym for summarise

units

(character)
The units in which the values in doseGrid are

format_func

(function)
The function used to format the range table.

use_values

(flag)
print the values associated with hyperparameters, or the symbols used to define the hyper-parameters. That is, for example, mu or 1.

fmt

(character)
the sprintf format string used to render numerical values. Ignored if use_values is FALSE.

biomarker_label

(character)
the term used to describe the biomarker

params

(character)
The names of the model parameters. See Usage Notes below.

preamble

(character)
the text that introduces the list of rules

theta

(character)
the LaTeX representation of the theta vector

eff_label

(character)
the term used to describe efficacy

target_label

(character)
the term used to describe the target toxicity rate

biomarker_units

(character)
the units in which the biomarker is measured

citation_text

(character)
the text used to cite Mozgunov & Jaki

citation_link

(character)
the link to Mozgunov & Jaki

indent

(integer)
the indent level of the current stopping rule list. Spaces with length indent * 4 will be prepended to the beginning of the rendered stopping rule list.

time_unit

(character)
the word used to describe units of time. See Usage Notes below.

ordinals

(character)
a character vector whose nth defines the word used as the written representation of the nth ordinal number.

dose_label

(character)
the term used to describe the target dose

fmt_string

(character)
the character string that defines the format of the output

Value

a character string that represents the object in markdown.

The markdown representation of the object, as a character string

a character string that represents the object in markdown.

A character string containing a LaTeX rendition of the object.

a character string that represents the object in markdown.

Usage Notes

label describes the trial's participants.

It should be a character vector of length 1 or 2. If of length 2, the first element describes a cohort_size of 1 and the second describes all other cohort_sizes. If of length 1, the character s is appended to the value when cohort_size is not 1.

The default value of col.names is c("Lower", "Upper", "Cohort size") and that of caption is "Defined by the dose to be used in the next cohort". These values can be overridden by passing col.names and caption in the function call.

The by default, the columns are labelled Lower, Upper and ⁠Cohort size⁠. The table's caption is ⁠Defined by the number of <tox_label[2]> so far observed⁠. These values can be overridden by passing col.names and caption in the function call.

label describes the trial's participants.

It should be a character vector of length 1 or 2. If of length 2, the first element describes a single participant and the second describes all other situations. If of length 1, the character s is appended to the value when the number of participants is not 1. The default values of col.names and caption vary depending on the summary requested. The default values can be overridden by passing col.names and caption in the function call.

params must be a character vector of length equal to that of x@mean (and x@cov). Its values represent the parameters of the model as entries in the vector theta, on the left-hand side of "~" in the definition of the prior. If named, names should be valid LaTeX, escaped as usual for R character variables. For example, "\\alpha" or "\\beta_0". If unnamed, names are constructed by pre-pending an escaped backslash to each value provided.

The default value of col.names is c("Min", "Max", "Increment") and that of caption is "Defined by highest dose administered so far". These values can be overridden by passing col.names and caption in the function call.

The default value of col.names is c("Min", "Max", "Increment") and that of caption is "Defined by number of DLTs reported so far". These values can be overridden by passing col.names and caption in the function call.

label defines how toxicities are described.

It should be a character vector of length 1 or 2. If of length 2, the first element describes a single toxicity and the second describes all other toxicity counts. If of length 1, the character s is appended to the value describing a single toxicity.

The default value of col.names is c("Min", "Max", "Increment") and that of caption is "Defined by number of DLTs in the current cohort". These values can be overridden by passing col.names and caption in the function call.

tox_label defines how toxicities are described.

This section describes the use of label and tox_label, collectively referred to as labels. A label should be a scalar or a vector of length 2. If a scalar, it is converted by adding a second element that is equal to the first, suffixed by s. For example, tox_label = "DLT" becomes tox_label = c("DLT", "DLTs"). The first element of the vector is used to describe a count of 1. The second is used in all other cases.

To use a BibTeX-style citation, specify (for example) ⁠citation_text = "@MOZGUNOV", citation_link = ""⁠.

label should be a character vector of length 1 or 2. If of length 2, the first element describes a count of 1 and the second describes all other counts. If of length 1, the character s is appended to the value when the count is not 1.

label and time_unit are, collectively, labels.

A label should be a character vector of length 1 or 2. If of length 2, the first element describes a count of 1 and the second describes all other counts. If of length 1, the character s is appended to the value when the count is not 1.

label describes the trial's participants.

label defines how toxicities are described.

tox_label defines how toxicities are described.

To use a BibTeX-style citation, specify (for example) ⁠citation_text = "@MOZGUNOV", citation_link = ""⁠.

label and time_unit are, collectively, labels.

label describes the trial's participants.

label defines how toxicities are described.

tox_label defines how toxicities are described.

To use a BibTeX-style citation, specify (for example) ⁠citation_text = "@MOZGUNOV", citation_link = ""⁠.

label and time_unit are, collectively, labels.

label describes the trial's participants.

label defines how toxicities are described.

tox_label defines how toxicities are described.

To use a BibTeX-style citation, specify (for example) ⁠citation_text = "@MOZGUNOV", citation_link = ""⁠.

label and time_unit are, collectively, labels.

`LogisticIndepBeta`

Description

LogisticIndepBeta is the class for the two-parameters logistic regression dose-limiting events (DLE) model with prior expressed in form of pseudo data. This model describes the relationship between the binary DLE responses and the dose levels. More specifically, it represents the relationship of the probabilities of the occurrence of a DLE for corresponding dose levels in log scale. This model is specified as

$p(x) = exp(phi1 + phi2 * log(x)) / (1 + exp(phi1 + phi2 * log(x)))$

where $p(x)$ is the probability of the occurrence of a DLE at dose $x$ . The two parameters of this model are the intercept $phi1$ and the slope $phi2$ . The LogisticIndepBeta inherits all slots from ModelTox class.

In the context of pseudo data, the following three arguments are used, binDLE, DLEdose and DLEweights. The DLEdose represents fixed dose levels at which the pseudo DLE responses binDLE are observed. DLEweights represents total number of subjects treated per each dose level in DLEdose. The binDLE represents the number of subjects observed with DLE per each dose level in DLEdose. Hence, all these three vectors must be of the same length and the order of the elements in any of the vectors binDLE, DLEdose and DLEweights must be kept, so that an element of a given vector corresponds to the elements of the remaining two vectors (see the example for more insight). Finally, since at least two DLE pseudo responses are needed to obtain prior modal estimates (same as the maximum likelihood estimates) for the model parameters, the binDLE, DLEdose and DLEweights must all be vectors of at least length 2.

Usage

LogisticIndepBeta(binDLE, DLEdose, DLEweights, data)

.DefaultLogisticIndepBeta()
LogisticIndepBeta(binDLE, DLEdose, DLEweights, data)

.DefaultLogisticIndepBeta()

Arguments

binDLE

(numeric)
the number of subjects observed with a DLE, the pseudo DLE responses, depending on dose levels DLEdose. Elements of binDLE must correspond to the elements of DLEdose and DLEweights.

DLEdose

(numeric)
dose levels for the pseudo DLE responses. Elements of DLEdose must correspond to the elements of binDLE and DLEweights.

DLEweights

(numeric)
the total number of subjects treated at each of the dose levels DLEdose, pseudo weights. Elements of DLEweights must correspond to the elements of binDLE and DLEdose.

data

(Data)
the input data to update estimates of the model parameters.

Details

The pseudo data can be interpreted as if we obtain some observations before the trial starts. It can be used to express our prior, i.e. the initial beliefs for the model parameters. The pseudo data is expressed in the following way. First, fix at least two dose levels, then ask for experts' opinion on how many subjects are to be treated at each of these dose levels and on the number of subjects observed with a DLE. At each dose level, the number of subjects observed with a DLE, divided by the total number of subjects treated, is the probability of the occurrence of a DLE at that particular dose level. The probabilities of the occurrence of a DLE based on this pseudo data are independent and they follow Beta distributions. Therefore, the joint prior probability density function of all these probabilities can be obtained. Hence, by a change of variable, the joint prior probability density function of the two parameters in this model can also be obtained. In addition, a conjugate joint prior density function of the two parameters in the model is used. For details about the form of all these joint prior and posterior probability density functions, please refer to Whitehead and Williamson (1998).

Slots

binDLE: (numeric)
a vector of total numbers of DLE responses. It must be at least of length 2 and the order of its elements must correspond to values specified in DLEdose and DLEweights.
DLEdose: (numeric)
a vector of the dose levels corresponding to It must be at least of length 2 and the order of its elements must correspond to values specified in binDLE and DLEweights.
DLEweights: (integer)
total number of subjects treated at each of the pseudo dose level DLEdose. It must be at least of length 2 and the order of its elements must correspond to values specified in binDLE and DLEdose.
phi1: (number)
the intercept of the model. This slot is used in output to display the resulting prior or posterior modal estimate of the intercept obtained based on the pseudo data and (if any) observed data/responses.
phi2: (number)
the slope of the model. This slot is used in output to display the resulting prior or posterior modal estimate of the slope obtained based on the pseudo data and (if any) the observed data/responses.
Pcov: (matrix)
refers to the 2x2 covariance matrix of the intercept ( $phi1$ ) and the slope parameters ( $phi2$ ) of the model. This is used in output to display the resulting prior and posterior covariance matrix of $phi1$ and $phi2$ obtained, based on the pseudo data and (if any) the observed data and responses. This slot is needed for internal purposes.

Note

Typically, end users will not use the .DefaultLogisticIndepBeta() function.

References

Whitehead J, Williamson D (1998). “Bayesian decision procedures based on logistic regression models for dose-finding studies.” Journal of Biopharmaceutical Statistics, 8(3), 445–467.

Examples

# Obtain prior modal estimates given the pseudo data.
# First we used an empty data set such that only the dose levels under
# investigations are given. In total, 12 dose levels are under investigation
# ranging from 25 to 300 mg with increments of 25 (i.e 25, 50, 75, ..., 300).
emptydata <- Data(doseGrid = seq(25, 300, 25))

# Fix two dose levels 25 and 300 mg (DLEdose).
# Total number of subjects treated in each of these levels is 3, (DLEweights).
# The number of subjects observed with a DLE is 1.05 at dose 25 mg and 1.8 at dose 300 mg (binDLE).
my_model1 <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEdose = c(25, 300),
  DLEweights = c(3, 3),
  data = emptydata
)

# Use observed DLE responses to obtain posterior modal estimates.
my_data <- Data(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = emptydata@doseGrid
)

my_model2 <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEdose = c(25, 300),
  DLEweights = c(3, 3),
  data = my_data
)
# Obtain prior modal estimates given the pseudo data.
# First we used an empty data set such that only the dose levels under
# investigations are given. In total, 12 dose levels are under investigation
# ranging from 25 to 300 mg with increments of 25 (i.e 25, 50, 75, ..., 300).
emptydata <- Data(doseGrid = seq(25, 300, 25))

# Fix two dose levels 25 and 300 mg (DLEdose).
# Total number of subjects treated in each of these levels is 3, (DLEweights).
# The number of subjects observed with a DLE is 1.05 at dose 25 mg and 1.8 at dose 300 mg (binDLE).
my_model1 <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEdose = c(25, 300),
  DLEweights = c(3, 3),
  data = emptydata
)

# Use observed DLE responses to obtain posterior modal estimates.
my_data <- Data(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = emptydata@doseGrid
)

my_model2 <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEdose = c(25, 300),
  DLEweights = c(3, 3),
  data = my_data
)

`LogisticKadane`

Description

LogisticKadane is the class for the logistic model in the parametrization of Kadane et al. (1980).

Usage

LogisticKadane(theta, xmin, xmax)

.DefaultLogisticKadane()
LogisticKadane(theta, xmin, xmax)

.DefaultLogisticKadane()

Arguments

theta

(proportion)
the target toxicity probability.

xmin

(number)
the minimum of the dose range.

xmax

(number)
the maximum of the dose range.

Details

Let rho0 = p(xmin) be the probability of a DLT at the minimum dose xmin, and let gamma be the dose with target toxicity probability theta, i.e. $p(gamma) = theta$ . Then it can easily be shown that the logistic regression model has intercept

$[gamma * logit(rho0) - xmin * logit(theta)] / [gamma - xmin]$

and slope

$[logit(theta) - logit(rho0)] / [gamma - xmin].$

The priors are

$gamma ~ Unif(xmin, xmax).$

and

$rho0 ~ Unif(0, theta).$

Slots

theta: (proportion)
the target toxicity probability.
xmin: (number)
the minimum of the dose range.
xmax: (number)
the maximum of the dose range.

Note

The slots of this class, required for creating the model, are the target toxicity, as well as the minimum and maximum of the dose range. Note that these can be different from the minimum and maximum of the dose grid in the data later on.

Typically, end-users will not use the .DefaultLogisticKadane() function.

References

Kadane JB, Dickey JM, Winkler RL, Smith WS, Peters SC (1980). “Interactive Elicitation of Opinion for a Normal Linear Model.” Journal of the American Statistical Association, 75(372), 845–854. ISSN 01621459, 1537274X. doi:10.2307/2287171. http://www.jstor.org/stable/2287171.

Examples

my_model <- LogisticKadane(theta = 0.33, xmin = 1, xmax = 200)
my_model <- LogisticKadane(theta = 0.33, xmin = 1, xmax = 200)

`LogisticKadaneBetaGamma`

Description

LogisticKadaneBetaGamma is the class for the logistic model in the parametrization of Kadane et al. (1980), using a beta and a gamma distribution as the model priors.

Usage

LogisticKadaneBetaGamma(theta, xmin, xmax, alpha, beta, shape, rate)

.DefaultLogisticKadaneBetaGamma()
LogisticKadaneBetaGamma(theta, xmin, xmax, alpha, beta, shape, rate)

.DefaultLogisticKadaneBetaGamma()

Arguments

theta

(proportion)
the target toxicity probability.

xmin

(number)
the minimum of the dose range.

xmax

(number)
the maximum of the dose range.

alpha

(number)
the first shape parameter of the Beta prior distribution rho0 = p(xmin) the probability of a DLT at the minimum dose xmin.

beta

(number)
the second shape parameter of the Beta prior distribution rho0 = p(xmin) the probability of a DLT at the minimum dose xmin.

shape

(number)
the shape parameter of the Gamma prior distribution gamma the dose with target toxicity probability theta.

rate

(number)
the rate parameter of the Gamma prior distribution gamma the dose with target toxicity probability theta.

Details

$[gamma * logit(rho0) - xmin * logit(theta)] / [gamma - xmin]$

and slope

$[logit(theta) - logit(rho0)] / [gamma - xmin].$

The prior for gamma, is

$gamma ~ Gamma(shape, rate).$

. The prior for rho0 = p(xmin), is

$rho0 ~ Beta(alpha, beta).$

Slots

theta: (proportion)
the target toxicity probability.
xmin: (number)
the minimum of the dose range.
xmax: (number)
the maximum of the dose range.
alpha: (number)
the first shape parameter of the Beta prior distribution of rho0 = p(xmin) the probability of a DLT at the minimum dose xmin.
beta: (number)
the second shape parameter of the Beta prior distribution of rho0 = p(xmin) the probability of a DLT at the minimum dose xmin.
shape: (number)
the shape parameter of the Gamma prior distribution of gamma the dose with target toxicity probability theta.
rate: (number)
the rate parameter of the Gamma prior distribution of gamma the dose with target toxicity probability theta.

Note

The slots of this class, required for creating the model, are the same as in the LogisticKadane class. In addition, the shape parameters of the Beta prior distribution of rho0 and the shape and rate parameters of the Gamma prior distribution of gamma, are required for creating the prior model.

Typically, end users will not use the .Default() function.

References

Examples

my_model <- LogisticKadaneBetaGamma(
  theta = 0.3,
  xmin = 0,
  xmax = 7,
  alpha = 1,
  beta = 19,
  shape = 0.5625,
  rate = 0.125
)
my_model <- LogisticKadaneBetaGamma(
  theta = 0.3,
  xmin = 0,
  xmax = 7,
  alpha = 1,
  beta = 19,
  shape = 0.5625,
  rate = 0.125
)

`LogisticLogNormal`

Description

LogisticLogNormal is the class for the usual logistic regression model with a bivariate normal prior on the intercept and log slope.

Usage

LogisticLogNormal(mean, cov, ref_dose = 1)

.DefaultLogisticLogNormal()
LogisticLogNormal(mean, cov, ref_dose = 1)

.DefaultLogisticLogNormal()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose $x*$ (strictly positive number).

Details

The covariate is the natural logarithm of the dose $x$ divided by the reference dose $x*$ , i.e.:

$logit[p(x)] = alpha0 + alpha1 * log(x/x*),$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ . The prior

$(alpha0, log(alpha1)) ~ Normal(mean, cov).$

Note

Typically, end users will not use the .DefaultLogisticLogNormal() function.

Examples

my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)
my_model
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)
my_model

`LogisticLogNormalGrouped`

Description

LogisticLogNormalGrouped is the class for a logistic regression model for both the mono and the combo arms of the simultaneous dose escalation design.

Usage

LogisticLogNormalGrouped(mean, cov, ref_dose = 1)

.DefaultLogisticLogNormalGrouped()
LogisticLogNormalGrouped(mean, cov, ref_dose = 1)

.DefaultLogisticLogNormalGrouped()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose $x*$ (strictly positive number).

Details

The continuous covariate is the natural logarithm of the dose $x$ divided by the reference dose $x*$ as in LogisticLogNormal. In addition, $I_c$ is a binary indicator covariate which is 1 for the combo arm and 0 for the mono arm. The model is then defined as:

$logit[p(x)] = (alpha0 + I_c * delta0) + (alpha1 + I_c * delta1) * log(x / x*),$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ , and delta0 and delta1 are the differences in the combo arm compared to the mono intercept and slope parameters alpha0 and alpha1. The prior is defined as

$(alpha0, log(delta0), log(alpha1), log(delta1)) ~ Normal(mean, cov).$

Note

Typically, end users will not use the .DefaultLogisticLogNormalGrouped() function.

Examples

my_model <- LogisticLogNormalGrouped(
  mean = c(-0.85, 0, 1, 0),
  cov = diag(1, 4),
  ref_dose = 50
)
my_model
my_model <- LogisticLogNormalGrouped(
  mean = c(-0.85, 0, 1, 0),
  cov = diag(1, 4),
  ref_dose = 50
)
my_model

`LogisticLogNormalMixture`

Description

LogisticLogNormalMixture is the class for standard logistic model with online mixture of two bivariate log normal priors.

Usage

LogisticLogNormalMixture(mean, cov, ref_dose, share_weight)

.DefaultLogisticLogNormalMixture()
LogisticLogNormalMixture(mean, cov, ref_dose, share_weight)

.DefaultLogisticLogNormalMixture()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose $x*$ (strictly positive number).

share_weight

(proportion)
the prior weight for the share component.

Details

This model can be used when data is arising online from the informative component of the prior, at the same time with the data of the trial of main interest. Formally, this is achieved by assuming that the probability of a DLT at dose $x$ is given by

$p(x) = \pi * p1(x) + (1 - \pi) * p2(x)$

where $\pi$ is the probability for the model $p(x)$ being the same as the model $p1(x)$ , which is the informative component of the prior. From this model data arises in parallel: at doses xshare, DLT information yshare is observed, in total nObsshare data points (see DataMixture). On the other hand, $1 - \pi$ , is the probability of a separate model $p2(x)$ . Both components have the same log normal prior distribution, which can be specified by the user, and which is inherited from the LogisticLogNormal class.

Slots

share_weight: (proportion)
the prior weight for the share component $p_{1}(x)$ .

Note

Typically, end users will not use the .DefaultLogNormalMixture() function.

Examples

# Decide on the dose grid and MCMC options.
dose_grid <- 1:80
my_options <- McmcOptions()

# Classic model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

empty_data <- Data(doseGrid = dose_grid)
prior_samples <- mcmc(empty_data, my_model, my_options)
plot(prior_samples, my_model, empty_data)

# Set up the mixture model and data share object.
model_share <- LogisticLogNormalMixture(
  share_weight = 0.1,
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

empty_data_share <- DataMixture(
  doseGrid = dose_grid,
  xshare = rep(c(10, 20, 40), each = 4),
  yshare = rep(0L, 12),
)

# Compare with the resulting prior model.
prior_samples_share <- mcmc(empty_data_share, model_share, my_options)
plot(prior_samples_share, model_share, empty_data_share)
# Decide on the dose grid and MCMC options.
dose_grid <- 1:80
my_options <- McmcOptions()

# Classic model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

empty_data <- Data(doseGrid = dose_grid)
prior_samples <- mcmc(empty_data, my_model, my_options)
plot(prior_samples, my_model, empty_data)

# Set up the mixture model and data share object.
model_share <- LogisticLogNormalMixture(
  share_weight = 0.1,
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

empty_data_share <- DataMixture(
  doseGrid = dose_grid,
  xshare = rep(c(10, 20, 40), each = 4),
  yshare = rep(0L, 12),
)

# Compare with the resulting prior model.
prior_samples_share <- mcmc(empty_data_share, model_share, my_options)
plot(prior_samples_share, model_share, empty_data_share)

`LogisticLogNormalOrdinal`

Description

LogisticLogNormalOrdinal is the class for a logistic lognormal CRM model using an ordinal toxicity scale.

Usage

LogisticLogNormalOrdinal(mean, cov, ref_dose)

.DefaultLogisticLogNormalOrdinal()
LogisticLogNormalOrdinal(mean, cov, ref_dose)

.DefaultLogisticLogNormalOrdinal()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose $x*$ (strictly positive number).

Note

Typically, end users will not use the .DefaultLogisticLogNormalOrdinal() function.

Examples

LogisticLogNormalOrdinal(
  mean = c(3, 4, 0),
  cov = diag(c(4, 3, 1)),
  ref_dose = 1
)
LogisticLogNormalOrdinal(
  mean = c(3, 4, 0),
  cov = diag(c(4, 3, 1)),
  ref_dose = 1
)

`LogisticLogNormalSub`

Description

LogisticLogNormalSub is the class for a standard logistic model with bivariate (log) normal prior with subtractive dose standardization.

Usage

LogisticLogNormalSub(mean, cov, ref_dose = 0)

.DefaultLogisticLogNormalSub()
LogisticLogNormalSub(mean, cov, ref_dose = 0)

.DefaultLogisticLogNormalSub()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose $x*$ .

Details

The covariate is the dose $x$ minus the reference dose $x*$ , i.e.:

$logit[p(x)] = alpha0 + alpha1 * (x - x*),$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ . The prior

$(alpha0, log(alpha1)) ~ Normal(mean, cov).$

Slots

params: (ModelParamsNormal)
bivariate normal prior parameters.
ref_dose: (number)
the reference dose $x*$ .

Note

Typically, end-users will not use the .DefaultLogisticLogNormalSub() function.

Examples

my_model <- LogisticLogNormalSub(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)
my_model <- LogisticLogNormalSub(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

`LogisticNormal`

Description

LogisticNormal is the class for the usual logistic regression model with a bivariate normal prior on the intercept and slope.

Usage

LogisticNormal(mean, cov, ref_dose = 1)

.DefaultLogisticNormal()
LogisticNormal(mean, cov, ref_dose = 1)

.DefaultLogisticNormal()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose $x*$ (strictly positive number).

Details

The covariate is the natural logarithm of the dose $x$ divided by the reference dose $x*$ , i.e.:

$logit[p(x)] = alpha0 + alpha1 * log(x/x*),$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ . The prior

$(alpha0, alpha1) ~ Normal(mean, cov).$

Note

Typically, end users will not use the .DefaultLogisticNormal() function.

Examples

# Define the dose-grid.
empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

my_model <- LogisticNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
)

my_options <- McmcOptions(burnin = 10, step = 2, samples = 100)

samples <- mcmc(empty_data, my_model, my_options)
samples
# Define the dose-grid.
empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

my_model <- LogisticNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
)

my_options <- McmcOptions(burnin = 10, step = 2, samples = 100)

samples <- mcmc(empty_data, my_model, my_options)
samples

`LogisticNormalFixedMixture`

Description

LogisticNormalFixedMixture is the class for standard logistic regression model with fixed mixture of multiple bivariate (log) normal priors on the intercept and slope parameters. The weights of the normal priors are fixed, hence no additional model parameters are introduced. This type of prior is often used to better approximate a given posterior distribution, or when the information is given in terms of a mixture.

Usage

LogisticNormalFixedMixture(components, weights, ref_dose, log_normal = FALSE)

.DefaultLogisticNormalFixedMixture()
LogisticNormalFixedMixture(components, weights, ref_dose, log_normal = FALSE)

.DefaultLogisticNormalFixedMixture()

Arguments

components

(list)
the specifications of the mixture components, a list with ModelParamsNormal objects for each bivariate (log) normal prior.

weights

(numeric)
the weights of the components; these must be positive and will be normalized to sum to 1.

ref_dose

(number)
the reference dose $x*$ (strictly positive number).

log_normal

(flag)
should a log normal prior be specified, such that the mean vectors and covariance matrices are valid for the intercept and log slope?

Details

The covariate is the natural logarithm of the dose $x$ divided by the reference dose $x*$ , i.e.:

$logit[p(x)] = alpha0 + alpha1 * log(x/x*),$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ . The prior

$(alpha0, alpha1) ~ w1 * Normal(mean1, cov1) + ... + wK * Normal(meanK, covK),$

if a normal prior is used and

$(alpha0, log(alpha1)) ~ w1 * Normal(mean1, cov1) + ... + wK * Normal(meanK, covK),$

if a log normal prior is used. The weights $w1, ..., wK$ of the components are fixed and sum to 1.

The slots of this class comprise a list with components parameters. Every single component contains the mean vector and the covariance matrix of bivariate normal distributions. Remaining slots are the weights of the components as well as the reference dose. Moreover, a special indicator slot specifies whether a log normal prior is used.

Slots

components: (list)
the specifications of the mixture components, a list with ModelParamsNormal objects for each bivariate (log) normal prior.
weights: (numeric)
the weights of the components; these must be positive and must sum to 1.
ref_dose: (positive_number)
the reference dose.
log_normal: (flag)
should a log normal prior be used, such that the mean vectors and covariance matrices are valid for the intercept and log slope?

Note

Typically, end-users will not use the .DefaultLogisticNormalFixedMixture() function.

Examples

my_model <- LogisticNormalFixedMixture(
  components = list(
    comp1 = ModelParamsNormal(
      mean = c(-0.85, 1),
      cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
    ),
    comp2 = ModelParamsNormal(
      mean = c(1, 1.5),
      cov = matrix(c(1.2, -0.45, -0.45, 0.6), nrow = 2)
    )
  ),
  weights = c(0.3, 0.7),
  ref_dose = 50
)
my_model <- LogisticNormalFixedMixture(
  components = list(
    comp1 = ModelParamsNormal(
      mean = c(-0.85, 1),
      cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
    ),
    comp2 = ModelParamsNormal(
      mean = c(1, 1.5),
      cov = matrix(c(1.2, -0.45, -0.45, 0.6), nrow = 2)
    )
  ),
  weights = c(0.3, 0.7),
  ref_dose = 50
)

`LogisticNormalMixture`

Description

LogisticNormalMixture is the class for standard logistic regression model with a mixture of two bivariate normal priors on the intercept and slope parameters.

Usage

LogisticNormalMixture(comp1, comp2, weightpar, ref_dose)

.DefaultLogisticNormalMixture()
LogisticNormalMixture(comp1, comp2, weightpar, ref_dose)

.DefaultLogisticNormalMixture()

Arguments

comp1

(ModelParamsNormal)
bivariate normal prior specification of the first component. See ModelParamsNormal for more details.

comp2

(ModelParamsNormal)
bivariate normal prior specification of the second component. See ModelParamsNormal for more details.

weightpar

(numeric)
the beta parameters for the weight of the first component. It must a be a named vector of length 2 with names a and b and with strictly positive values.

ref_dose

(number)
the reference dose $x*$ (strictly positive number).

Details

The covariate is the natural logarithm of the dose $x$ divided by the reference dose $x*$ , i.e.:

$logit[p(x)] = alpha0 + alpha1 * log(x/x*),$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ . The prior

$(alpha0, alpha1) ~ w * Normal(mean1, cov1) + (1 - w) * Normal(mean2, cov2).$

The weight w for the first component is assigned a beta prior B(a, b).

Slots

comp1: (ModelParamsNormal)
bivariate normal prior specification of the first component.
comp2: (ModelParamsNormal)
bivariate normal prior specification of the second component.
weightpar: (numeric)
the beta parameters for the weight of the first component. It must a be a named vector of length 2 with names a and b and with strictly positive values.
ref_dose: (positive_number)
the reference dose.

Note

The weight of the two normal priors is a model parameter, hence it is a flexible mixture. This type of prior is often used with a mixture of a minimal informative and an informative component, in order to make the CRM more robust to data deviations from the informative component.

Typically, end-users will not use the .DefaultLogisticNormalMixture() function.

Examples

my_model <- LogisticNormalMixture(
  comp1 = ModelParamsNormal(
    mean = c(-0.85, 1),
    cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
  ),
  comp2 = ModelParamsNormal(
    mean = c(1, 1.5),
    cov = matrix(c(1.2, -0.45, -0.45, 0.6), nrow = 2)
  ),
  weightpar = c(a = 1, b = 1),
  ref_dose = 50
)
my_model <- LogisticNormalMixture(
  comp1 = ModelParamsNormal(
    mean = c(-0.85, 1),
    cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
  ),
  comp2 = ModelParamsNormal(
    mean = c(1, 1.5),
    cov = matrix(c(1.2, -0.45, -0.45, 0.6), nrow = 2)
  ),
  weightpar = c(a = 1, b = 1),
  ref_dose = 50
)

Shorthand for Logit Function

Description

Computes the logit transformation.

Usage

logit(x)
logit(x)

Arguments

x

(numeric)
the function argument.

Value

The logit of x, computed as log(x / (1 - x)).

Examples

logit(0.5)
logit(c(0.1, 0.5, 0.9))
logit(0.5)
logit(c(0.1, 0.5, 0.9))

Helper Function for Value Matching with Tolerance

Description

This is a modified version of match() that supports tolerance.

Usage

match_within_tolerance(x, table, tolerance = 1e-10)
match_within_tolerance(x, table, tolerance = 1e-10)

Arguments

x

(numeric)
the values to be matched.

table

(numeric)
the values to be matched against.

tolerance

(number)
the numerical tolerance for comparison.

Value

An integer vector of the same length as x giving the position in table of the first match, or an empty integer vector if table is empty. NA is returned for values in x that have no match.

Examples

match_within_tolerance(c(0.1, 0.2, 0.3), c(0.10000001, 0.5, 0.3))
match_within_tolerance(1.5, numeric(0))
match_within_tolerance(c(0.1, 0.2, 0.3), c(0.10000001, 0.5, 0.3))
match_within_tolerance(1.5, numeric(0))

Determine the Maximum Possible Next Dose

Description

This function determines the upper limit of the next dose based on the incrementsand the data.

Usage

maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelative,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeDLT,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeDLTCurrent,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeParts,DataParts'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsDoseLevels,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsHSRBeta,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsMin,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsMin,DataOrdinal'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsOrdinal,DataOrdinal'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsMaxToxProb,DataOrdinal'
maxDose(increments, data, model, samples, ...)

## S4 method for signature 'IncrementsMaxToxProb,Data'
maxDose(increments, data, model, samples, ...)
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelative,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeDLT,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeDLTCurrent,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeParts,DataParts'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsDoseLevels,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsHSRBeta,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsMin,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsMin,DataOrdinal'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsOrdinal,DataOrdinal'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsMaxToxProb,DataOrdinal'
maxDose(increments, data, model, samples, ...)

## S4 method for signature 'IncrementsMaxToxProb,Data'
maxDose(increments, data, model, samples, ...)

Arguments

increments

(Increments)
the rule for the next best dose.

data

(Data)
input data.

...

additional arguments without method dispatch.

model

(GeneralModel)
The model on which probabilities will be based

samples

(Samples)
The MCMC samples to which model will be applied

Value

A number, the maximum possible next dose.

Functions

maxDose(increments = IncrementsRelative, data = Data): determine the maximum possible next dose based on relative increments.
maxDose(increments = IncrementsRelativeDLT, data = Data): determine the maximum possible next dose based on relative increments determined by DLTs so far.
maxDose(increments = IncrementsRelativeDLTCurrent, data = Data): determine the maximum possible next dose based on relative increments determined by DLTs in the current cohort.
maxDose(increments = IncrementsRelativeParts, data = DataParts): determine the maximum possible next dose based on relative increments as well as part 1 and beginning of part 2.
maxDose(increments = IncrementsDoseLevels, data = Data): determine the maximum possible next dose based on the number of dose grid levels. That is, the max dose is determined as the one which level is equal to: base dose level + level increment. The base dose level is the level of the last dose in grid or the level of the maximum dose applied, which is defined in increments object. Find out more in IncrementsDoseLevels.
maxDose(increments = IncrementsHSRBeta, data = Data): determine the maximum possible next dose for escalation.
maxDose(increments = IncrementsMin, data = Data): determine the maximum possible next dose based on multiple increment rules, taking the minimum across individual increments.
maxDose(increments = IncrementsMin, data = DataOrdinal): determine the maximum possible next dose based on multiple increment rules, taking the minimum across individual increments.
maxDose(increments = IncrementsOrdinal, data = DataOrdinal): determine the maximum possible next dose in an ordinal CRM trial
maxDose(increments = IncrementsMaxToxProb, data = DataOrdinal): determine the maximum possible next dose based on the probability of toxicity
maxDose(increments = IncrementsMaxToxProb, data = Data): determine the maximum possible next dose based on the probability of toxicity

Examples

# Example of usage for `IncrementsRelative` maxDose class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 8, 10:40)
)

# Define a rule for dose increments which allows for:
#  - doubling the dose if the last dose was below 20,
#  - increasing the dose by 33% of the last dose, only if the last dose was
#    above or equal to 20.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Based on the rule above, the maximum dose allowed is:
max_dose <- maxDose(my_increments, data = my_data)
# Example of usage for `IncrementsRelativeDLT` maxDose class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 8, seq(from = 10, to = 80, by = 2))
)

# Define a rule for dose increments which allows for:
#  - doubling the dose if no DLTs were yet observed,
#  - increasing the dose by 33% if 1 or 2 DLTs were already observed,
#  - increasing the dose by 20% if at least 3 DLTs were already observed.
my_increments <- IncrementsRelativeDLT(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)

# Based on the rule above, the maximum dose allowed is:
max_dose <- maxDose(my_increments, data = my_data)
# Example of usage for `IncrementsRelativeDLTCurrent` maxDose class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Define a rule for dose increments which allows for:
#  - doubling the dose if no DLTs were observed in current (i.e. last) cohort,
#  - only increasing the dose by 33% if 1 or 2 DLTs were observed in current cohort,
#  - only increasing the dose by 20% if at least 3 DLTs were observed in current cohort.
my_increments <- IncrementsRelativeDLTCurrent(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)

# Based on the rule above, the maximum dose allowed is:
max_dose <- maxDose(my_increments, data = my_data)
# Example of usage for `IncrementsRelativeParts` maxDose class.

# Create an object of class `DataParts`.
my_data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  ID = 1:3,
  cohort = 1:3,
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 10),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)

my_increments <- IncrementsRelativeParts(
  dlt_start = 0,
  clean_start = 1
)

max_dose <- maxDose(my_increments, data = my_data)
# Example of usage for `IncrementsDoseLevels` maxDose class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 8, 8, 12, 12, 12, 16, 16, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1),
  ID = 1:14,
  cohort = c(1, 2, 3, 4, 5, 6, 6, 7, 7, 7, 8, 8, 9, 9),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 8, 10:30)
)

# In this first example we define a rule for dose increments which allows for
# maximum skip one dose level, that is 2 dose levels higher than the last dose
# given.
my_increments_1 <- IncrementsDoseLevels(levels = 2, basis_level = "last")

# Based on the rule above, the maximum dose allowed is:
max_dose_1 <- maxDose(my_increments_1, data = my_data)

# In this second example we define a rule for dose increments which allows for
# maximum skip one dose level, that is 2 dose levels higher than the max dose
# given.
my_increments_2 <- IncrementsDoseLevels(levels = 2, basis_level = "max")

# Based on the rule above, the maximum dose allowed is:
max_dose_2 <- maxDose(my_increments_2, data = my_data)
# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8, 6, 6, 6),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 8, seq(from = 10, to = 80, by = 2))
)

# In this example we define a rule for dose increments that limits the further
# dose escalation to doses below 6, because dose 6 is above the probability
# toxicity threshold.
my_increments <- IncrementsHSRBeta(target = 0.3, prob = 0.95)

# Based on the rule above, we then calculate the maximum dose allowed.
my_next_max_dose <- maxDose(my_increments, data = my_data)
# Example of usage for `IncrementsRelativeDLTCurrent` maxDose class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 8, 10:80)
)

# Here, we combine two different increment rules.

# The first rule allows for:
#  - doubling the dose if no DLTs were observed at the current dose,
#  - increasing the dose by 33% if 1 or 2 DLTs were observed at the current dose,
#  - increasing the dose by 22% if 3 or more DLTs were observed.
my_increments_1 <- IncrementsRelativeDLT(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)

# The second rule allows for:
#  - doubling the dose if the current dose is <20,
#  - increasing the dose by 33% if the current dose is >=20.
my_increments_2 <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Finally, the maximum dose allowed is computed by taking the minimum dose from
# the maximum doses computed by the two rules.
my_increments <- IncrementsMin(
  increments_list = list(my_increments_1, my_increments_2)
)
max_dose <- maxDose(my_increments, my_data)
maxDose(
  increments = IncrementsOrdinal(2L, .DefaultIncrementsRelative()),
  data = .DefaultDataOrdinal()
)
model <- LogisticLogNormalOrdinal(
  mean = c(0.25, 0.15, 0.5),
  cov = matrix(c(1.5, 0, 0, 0, 2, 0, 0, 0, 1), nrow = 3),
  ref_dose = 30
)

emptyData <- DataOrdinal(
  doseGrid = c(1, 3, 9, 25, 50, 75, 100),
  yCategories = c("No tox" = 0L, "DLAE" = 1L, "CRS" = 2L)
)

# For warning regarding tox, see issue #748 https://github.com/openpharma/crmPack/issues/748
suppressWarnings({
  samples <- mcmc(emptyData, model, .DefaultMcmcOptions())
})
toxIncrements <- IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "CRS" = 0.05))
maxDose(toxIncrements, emptyData, model, samples)
model <- LogisticLogNormalOrdinal(
  mean = c(0.25, 0.15, 0.5),
  cov = matrix(c(1.5, 0, 0, 0, 2, 0, 0, 0, 1), nrow = 3),
  ref_dose = 30
)

emptyData <- DataOrdinal(
  doseGrid = c(1, 3, 9, 25, 50, 75, 100),
  yCategories = c("No tox" = 0L, "DLAE" = 1L, "CRS" = 2L)
)

# For warning regarding tox, see issue #748 https://github.com/openpharma/crmPack/issues/748
suppressWarnings({
  samples <- mcmc(emptyData, model, .DefaultMcmcOptions())
})
toxIncrements <- IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "CRS" = 0.05))
maxDose(toxIncrements, emptyData, model, samples)
# Example of usage for `IncrementsRelative` maxDose class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 8, 10:40)
)

# Define a rule for dose increments which allows for:
#  - doubling the dose if the last dose was below 20,
#  - increasing the dose by 33% of the last dose, only if the last dose was
#    above or equal to 20.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Based on the rule above, the maximum dose allowed is:
max_dose <- maxDose(my_increments, data = my_data)
# Example of usage for `IncrementsRelativeDLT` maxDose class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 8, seq(from = 10, to = 80, by = 2))
)

# Define a rule for dose increments which allows for:
#  - doubling the dose if no DLTs were yet observed,
#  - increasing the dose by 33% if 1 or 2 DLTs were already observed,
#  - increasing the dose by 20% if at least 3 DLTs were already observed.
my_increments <- IncrementsRelativeDLT(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)

# Based on the rule above, the maximum dose allowed is:
max_dose <- maxDose(my_increments, data = my_data)
# Example of usage for `IncrementsRelativeDLTCurrent` maxDose class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Define a rule for dose increments which allows for:
#  - doubling the dose if no DLTs were observed in current (i.e. last) cohort,
#  - only increasing the dose by 33% if 1 or 2 DLTs were observed in current cohort,
#  - only increasing the dose by 20% if at least 3 DLTs were observed in current cohort.
my_increments <- IncrementsRelativeDLTCurrent(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)

# Based on the rule above, the maximum dose allowed is:
max_dose <- maxDose(my_increments, data = my_data)
# Example of usage for `IncrementsRelativeParts` maxDose class.

# Create an object of class `DataParts`.
my_data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  ID = 1:3,
  cohort = 1:3,
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 10),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)

my_increments <- IncrementsRelativeParts(
  dlt_start = 0,
  clean_start = 1
)

max_dose <- maxDose(my_increments, data = my_data)
# Example of usage for `IncrementsDoseLevels` maxDose class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 8, 8, 12, 12, 12, 16, 16, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1),
  ID = 1:14,
  cohort = c(1, 2, 3, 4, 5, 6, 6, 7, 7, 7, 8, 8, 9, 9),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 8, 10:30)
)

# In this first example we define a rule for dose increments which allows for
# maximum skip one dose level, that is 2 dose levels higher than the last dose
# given.
my_increments_1 <- IncrementsDoseLevels(levels = 2, basis_level = "last")

# Based on the rule above, the maximum dose allowed is:
max_dose_1 <- maxDose(my_increments_1, data = my_data)

# In this second example we define a rule for dose increments which allows for
# maximum skip one dose level, that is 2 dose levels higher than the max dose
# given.
my_increments_2 <- IncrementsDoseLevels(levels = 2, basis_level = "max")

# Based on the rule above, the maximum dose allowed is:
max_dose_2 <- maxDose(my_increments_2, data = my_data)
# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8, 6, 6, 6),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 8, seq(from = 10, to = 80, by = 2))
)

# In this example we define a rule for dose increments that limits the further
# dose escalation to doses below 6, because dose 6 is above the probability
# toxicity threshold.
my_increments <- IncrementsHSRBeta(target = 0.3, prob = 0.95)

# Based on the rule above, we then calculate the maximum dose allowed.
my_next_max_dose <- maxDose(my_increments, data = my_data)
# Example of usage for `IncrementsRelativeDLTCurrent` maxDose class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, 8, 10:80)
)

# Here, we combine two different increment rules.

# The first rule allows for:
#  - doubling the dose if no DLTs were observed at the current dose,
#  - increasing the dose by 33% if 1 or 2 DLTs were observed at the current dose,
#  - increasing the dose by 22% if 3 or more DLTs were observed.
my_increments_1 <- IncrementsRelativeDLT(
  intervals = c(0, 1, 3),
  increments = c(1, 0.33, 0.2)
)

# The second rule allows for:
#  - doubling the dose if the current dose is <20,
#  - increasing the dose by 33% if the current dose is >=20.
my_increments_2 <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Finally, the maximum dose allowed is computed by taking the minimum dose from
# the maximum doses computed by the two rules.
my_increments <- IncrementsMin(
  increments_list = list(my_increments_1, my_increments_2)
)
max_dose <- maxDose(my_increments, my_data)
maxDose(
  increments = IncrementsOrdinal(2L, .DefaultIncrementsRelative()),
  data = .DefaultDataOrdinal()
)
model <- LogisticLogNormalOrdinal(
  mean = c(0.25, 0.15, 0.5),
  cov = matrix(c(1.5, 0, 0, 0, 2, 0, 0, 0, 1), nrow = 3),
  ref_dose = 30
)

emptyData <- DataOrdinal(
  doseGrid = c(1, 3, 9, 25, 50, 75, 100),
  yCategories = c("No tox" = 0L, "DLAE" = 1L, "CRS" = 2L)
)

# For warning regarding tox, see issue #748 https://github.com/openpharma/crmPack/issues/748
suppressWarnings({
  samples <- mcmc(emptyData, model, .DefaultMcmcOptions())
})
toxIncrements <- IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "CRS" = 0.05))
maxDose(toxIncrements, emptyData, model, samples)
model <- LogisticLogNormalOrdinal(
  mean = c(0.25, 0.15, 0.5),
  cov = matrix(c(1.5, 0, 0, 0, 2, 0, 0, 0, 1), nrow = 3),
  ref_dose = 30
)

emptyData <- DataOrdinal(
  doseGrid = c(1, 3, 9, 25, 50, 75, 100),
  yCategories = c("No tox" = 0L, "DLAE" = 1L, "CRS" = 2L)
)

# For warning regarding tox, see issue #748 https://github.com/openpharma/crmPack/issues/748
suppressWarnings({
  samples <- mcmc(emptyData, model, .DefaultMcmcOptions())
})
toxIncrements <- IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "CRS" = 0.05))
maxDose(toxIncrements, emptyData, model, samples)

Calculate Maximum Number of Backfill Patients

Description

Calculates the maximum number of backfill patients that can be recruited based on the recruitment rule and the active cohort size.

Usage

maxRecruits(object, active_cohort_size, ...)

## S4 method for signature 'RecruitmentUnlimited'
maxRecruits(object, active_cohort_size, ...)

## S4 method for signature 'RecruitmentRatio'
maxRecruits(object, active_cohort_size, ...)
maxRecruits(object, active_cohort_size, ...)

## S4 method for signature 'RecruitmentUnlimited'
maxRecruits(object, active_cohort_size, ...)

## S4 method for signature 'RecruitmentRatio'
maxRecruits(object, active_cohort_size, ...)

Arguments

object

(Recruitment)
the recruitment rule.

active_cohort_size

(integer)
the current size of the active dose escalation cohort.

...

further arguments (not used).

Value

The maximum number of backfill patients as an integer.

Functions

maxRecruits(RecruitmentUnlimited): method for RecruitmentUnlimited class. Returns a very large number (practically unlimited).
maxRecruits(RecruitmentRatio): method for RecruitmentRatio class. Returns ceiling(ratio * active_cohort_size).

Examples

# Create a RecruitmentUnlimited object
recruitment <- RecruitmentUnlimited()

# Calculate maximum recruits for various active cohort sizes
max_recruits_10 <- maxRecruits(recruitment, active_cohort_size = 10)
print(max_recruits_10) # Returns 1e6

max_recruits_100 <- maxRecruits(recruitment, active_cohort_size = 100)
print(max_recruits_100) # Still returns 1e6 (unlimited)

# With RecruitmentUnlimited, the active_cohort_size is ignored
# Create a RecruitmentRatio object with ratio 0.5
recruitment <- RecruitmentRatio(ratio = 0.5)

# Calculate maximum recruits based on active cohort size
# For active cohort of 10: ceiling(0.5 * 10) = 5
max_recruits_10 <- maxRecruits(recruitment, active_cohort_size = 10)
print(max_recruits_10) # 5

# For active cohort of 7: ceiling(0.5 * 7) = ceiling(3.5) = 4
max_recruits_7 <- maxRecruits(recruitment, active_cohort_size = 7)
print(max_recruits_7) # 4

# For active cohort of 15: ceiling(0.5 * 15) = ceiling(7.5) = 8
max_recruits_15 <- maxRecruits(recruitment, active_cohort_size = 15)
print(max_recruits_15) # 8
# Create a RecruitmentUnlimited object
recruitment <- RecruitmentUnlimited()

# Calculate maximum recruits for various active cohort sizes
max_recruits_10 <- maxRecruits(recruitment, active_cohort_size = 10)
print(max_recruits_10) # Returns 1e6

max_recruits_100 <- maxRecruits(recruitment, active_cohort_size = 100)
print(max_recruits_100) # Still returns 1e6 (unlimited)

# With RecruitmentUnlimited, the active_cohort_size is ignored
# Create a RecruitmentRatio object with ratio 0.5
recruitment <- RecruitmentRatio(ratio = 0.5)

# Calculate maximum recruits based on active cohort size
# For active cohort of 10: ceiling(0.5 * 10) = 5
max_recruits_10 <- maxRecruits(recruitment, active_cohort_size = 10)
print(max_recruits_10) # 5

# For active cohort of 7: ceiling(0.5 * 7) = ceiling(3.5) = 4
max_recruits_7 <- maxRecruits(recruitment, active_cohort_size = 7)
print(max_recruits_7) # 4

# For active cohort of 15: ceiling(0.5 * 15) = ceiling(7.5) = 8
max_recruits_15 <- maxRecruits(recruitment, active_cohort_size = 15)
print(max_recruits_15) # 8

"MAX" Combination of Cohort Size Rules

Description

This function combines cohort size rules by taking the maximum of all sizes.

Usage

maxSize(...)

## S4 method for signature 'CohortSize'
maxSize(...)
maxSize(...)

## S4 method for signature 'CohortSize'
maxSize(...)

Arguments

...

Objects of class CohortSize.

Value

The combination as an object of class CohortSizeMax.

Functions

maxSize(CohortSize): The method combining cohort size rules by taking maximum.

Examples

# Here is the rule for:
#   having cohort of size 1 for doses <30
#   and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 30), cohort_size = c(1, 3))

# Here is the rule for:
#   having cohort of size 1 until no DLT were observed
#   and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))

# This is combining the two rules above by taking the maximum of the sample sizes of
# the single rules
mySize <- maxSize(mySize1, mySize2)
# Here is the rule for:
#   having cohort of size 1 for doses <30
#   and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 30), cohort_size = c(1, 3))

# Here is the rule for:
#   having cohort of size 1 until no DLT were observed
#   and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))

# This is combining the two rules above by taking the maximum of the sample sizes of
# the single rules
mySize <- maxSize(mySize1, mySize2)

Obtaining Posterior Samples for all Model Parameters

Description

This is the function that actually runs the JAGS MCMC machinery to produce posterior samples from all model parameters and required derived values. It is a generic function, so that customized versions may be conveniently defined for specific subclasses of GeneralData, GeneralModel, and McmcOptions input.

Usage

mcmc(data, model, options, ...)

## S4 method for signature 'GeneralData,GeneralModel,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'GeneralData,DualEndpointRW,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'GeneralData,DualEndpointBeta,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'GeneralData,DualEndpointEmax,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'GeneralData,OneParLogNormalPrior,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'GeneralData,OneParExpPrior,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'DataMixture,GeneralModel,McmcOptions'
mcmc(
  data,
  model,
  options,
  from_prior = data@nObs == 0L & data@nObsshare == 0L,
  ...
)

## S4 method for signature 'Data,LogisticIndepBeta,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataDual,Effloglog,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataDual,EffFlexi,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataOrdinal,LogisticLogNormalOrdinal,McmcOptions'
mcmc(data, model, options, ...)
mcmc(data, model, options, ...)

## S4 method for signature 'GeneralData,GeneralModel,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'GeneralData,DualEndpointRW,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'GeneralData,DualEndpointBeta,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'GeneralData,DualEndpointEmax,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'GeneralData,OneParLogNormalPrior,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'GeneralData,OneParExpPrior,McmcOptions'
mcmc(data, model, options, from_prior = data@nObs == 0L, ...)

## S4 method for signature 'DataMixture,GeneralModel,McmcOptions'
mcmc(
  data,
  model,
  options,
  from_prior = data@nObs == 0L & data@nObsshare == 0L,
  ...
)

## S4 method for signature 'Data,LogisticIndepBeta,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataDual,Effloglog,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataDual,EffFlexi,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataOrdinal,LogisticLogNormalOrdinal,McmcOptions'
mcmc(data, model, options, ...)

Arguments

data

(GeneralData)
an input data.

model

(GeneralModel)
an input model.

options

(McmcOptions)
MCMC options.

...

not used.

from_prior

(flag)
sample from the prior only? Default to TRUE when number of observations in data is 0. For some models it might be necessary to specify it manually here though.

Value

The posterior samples, an object of class Samples.

Functions

mcmc(data = GeneralData, model = GeneralModel, options = McmcOptions): Standard method which uses JAGS.
mcmc(data = GeneralData, model = DualEndpointRW, options = McmcOptions): Standard method which uses JAGS. For the DualEndpointRW model, it is required that there are at least two (in case of random walk prior of the first order on the biomarker level) or three doses in the grid.
mcmc(data = GeneralData, model = DualEndpointBeta, options = McmcOptions): Standard method which uses JAGS. For the DualEndpointBeta model, it is required that the value of ref_dose_beta slot is greater than the maximum dose in a grid. This requirement comes from definition of the beta function that is used to model dose-biomarker relationship in DualEndpointBeta model. The other requirement is that there must be at least one dose in the grid.
mcmc(data = GeneralData, model = DualEndpointEmax, options = McmcOptions): Standard method which uses JAGS. For the DualEndpointEmax model, it is required that there is at least one dose in the grid.
mcmc(data = GeneralData, model = OneParLogNormalPrior, options = McmcOptions): Standard method which uses JAGS. For the OneParLogNormalPrior model, it is required that the length of skeleton prior probabilities vector should be equal to the length of the number of doses.
mcmc(data = GeneralData, model = OneParExpPrior, options = McmcOptions): Standard method which uses JAGS. For the OneParExpPrior model, it is required that the length of skeleton prior probabilities vector should be equal to the length of the number of doses.
mcmc(data = DataMixture, model = GeneralModel, options = McmcOptions): Method for DataMixture with different from_prior default. Samples from the prior only when both the number of observations and the number of shared observations are zero.
mcmc(data = Data, model = LogisticIndepBeta, options = McmcOptions): Obtain posterior samples for the model parameters based on the pseudo LogisticIndepBeta DLE model. The joint prior and posterior probability density function of the intercept $\phi_1$ (phi1) and the slope $\phi_2$ (phi2) are given in Whitehead and Williamson (1998). However, since asymptotically, the joint posterior probability density will be bivariate normal, we use the bivariate normal distribution to generate posterior samples of the intercept and the slope parameters. For the prior samples of the intercept and the slope, a bivariate normal distribution with mean and the covariance matrix given in Whitehead and Williamson (1998) is used.
mcmc(data = DataDual, model = Effloglog, options = McmcOptions): Obtain the posterior samples for the model parameters in the Effloglog model. Given the value of $\nu$ , the precision of the efficacy responses, the joint prior or the posterior probability of the intercept $\theta_1$ (theta1) and the slope $\theta_2$ (theta2) is a bivariate normal distribution. The $\nu$ (nu), the precision of the efficacy responses is either a fixed value or has a gamma distribution. If a gamma distribution is used, the samples of nu will be first generated. Then the mean of the nu samples will be used to generate samples of the intercept and slope parameters of the model.
mcmc(data = DataDual, model = EffFlexi, options = McmcOptions): Obtain the posterior samples for the estimates in the EffFlexi model. This is the MCMC procedure based on what is described in Lang and Brezger (2004) such that samples of the mean efficacy responses at all dose levels, samples of sigma2 $\sigma^2$ , the variance of the efficacy response and samples of sigma2betaW $\sigma^2_{\beta_W}$ , the variance of the random walk model will be generated. Please refer to Lang and Brezger (2004) for the procedures and the form of the joint prior and posterior probability density for the mean efficacy responses. In addition, both sigma2 and sigma2betaW can be fixed or have an inverse-gamma prior and posterior distribution. Therefore, if the inverse gamma distribution(s) are used, the parameters in the distribution will be first updated and then samples of sigma2 and sigma2betaW will be generated using the updated parameters.
mcmc( data = DataOrdinal, model = LogisticLogNormalOrdinal, options = McmcOptions ): Obtain the posterior samples for the model parameters in the LogisticLogNormalOrdinal model.

The generic mcmc method returns a Samples object with elements of the data slot named alpha[1], alpha[2], ..., alpha[k] and beta when passed a LogisticLogNormalOrdinal object. This makes the "alpha elements" awkward to access and is inconsistent with other model objects. So rename the alpha elements to alpha1, alpha2, ..., ⁠alpha<k>⁠ for ease and consistency.

Note

The type of Random Number Generator (RNG) and its initial seed used by JAGS are taken from the options argument. If no initial values are supplied (i.e RNG kind or seed slot in options has NA), then they will be generated automatically by JAGS.

References

Lang S, Brezger A (2004). “Bayesian P-Splines.” Journal of Computational and Graphical Statistics, 13(1), 183–212. ISSN 10618600. doi:10.1198/1061860043010. http://www.jstor.org/stable/1391151.

Whitehead J, Williamson D (1998). “Bayesian decision procedures based on logistic regression models for dose-finding studies.” Journal of Biopharmaceutical Statistics, 8(3), 445–467.

Examples

# Create some data from the class `Data`.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Sample from the posterior distribution.
my_options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 1000
)

samples <- mcmc(data = my_data, model = my_model, options = my_options)
samples
# Create some data from the class `DataDual`.
plcb <- 0.001
my_data <- DataDual(
  w = c(13, 77, 86, 26, 27, 36, 37, 97, 21, 49, 87, 48),
  x = c(plcb, 25, 25, 25, plcb, 50, 50, 50, plcb, 100, 100, 100),
  y = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L),
  doseGrid = c(plcb, seq(25, 300, 25)),
  placebo = TRUE,
  ID = 1:12,
  cohort = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L)
)

# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  sigma2betaW = 0.01,
  rw1 = TRUE
)

# Sample from the posterior distribution.
my_options <- McmcOptions(
  burnin = 50,
  step = 2,
  samples = 4,
  rng_kind = "Mersenne-Twister",
  rng_seed = 1
)

samples <- mcmc(data = my_data, model = my_model, options = my_options)
samples
# Create some data from the class `DataDual`.
plcb <- 0.001
my_data <- DataDual(
  w = c(13, 77, 86, 26, 27, 36, 37, 97, 21, 49, 87, 48),
  x = c(plcb, 25, 25, 25, plcb, 50, 50, 50, plcb, 100, 100, 100),
  y = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L),
  doseGrid = c(plcb, seq(25, 300, 25)),
  placebo = TRUE,
  ID = 1:12,
  cohort = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L)
)

# Initialize the CRM model.
my_model <- DualEndpointBeta(
  mean = c(0, 1),
  cov = diag(2),
  ref_dose = 2,
  use_log_dose = FALSE,
  sigma2W = c(a = 1, b = 2),
  rho = c(a = 1.5, b = 2.5),
  E0 = 2,
  Emax = 50,
  delta1 = 6,
  mode = 9,
  ref_dose_beta = my_data@doseGrid[my_data@nGrid] + 10
)

# Sample from the posterior distribution.
my_options <- McmcOptions(
  burnin = 50,
  step = 2,
  samples = 4,
  rng_kind = "Mersenne-Twister",
  rng_seed = 1
)

samples <- mcmc(data = my_data, model = my_model, options = my_options)
samples
##obtain mcmc DLE samples given the data, LogisticIndepBeta (DLE model) and mcmc simulations options
## data must be of 'Data' class
data <- Data(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(25, 300, 25)
)
## model must be of 'LogisticIndepBeta' class
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## options must be ''McmcOptions' class
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
set.seed(94)
samples <- mcmc(data = data, model = model, options = options)
# nolint start
##obtain mcmc efficacy samples given the data, 'Effloglog' model (efficacy model) and
## mcmc simulations options data must be of 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## model must be of 'Effloglog' class
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)

## options must be ''McmcOptions' class
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
set.seed(94)
samples <- mcmc(data = data, model = Effmodel, options = options)
# nolint end
## obtain mcmc efficacy samples given the data, 'EffFlexi' model (efficacy model) and
## mcmc simulations options
## data must be of 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25)
)
## model must be of 'EffFlexi' class

effmodel <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)

## options must be ''McmcOptions' class
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
set.seed(94)
samples <- mcmc(data = data, model = effmodel, options = options)
ordinal_data <- .DefaultDataOrdinal()
ordinal_model <- .DefaultLogisticLogNormalOrdinal()
mcmc_options <- .DefaultMcmcOptions()

samples <- mcmc(ordinal_data, ordinal_model, mcmc_options)
# Create some data from the class `Data`.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Sample from the posterior distribution.
my_options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 1000
)

samples <- mcmc(data = my_data, model = my_model, options = my_options)
samples
# Create some data from the class `DataDual`.
plcb <- 0.001
my_data <- DataDual(
  w = c(13, 77, 86, 26, 27, 36, 37, 97, 21, 49, 87, 48),
  x = c(plcb, 25, 25, 25, plcb, 50, 50, 50, plcb, 100, 100, 100),
  y = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L),
  doseGrid = c(plcb, seq(25, 300, 25)),
  placebo = TRUE,
  ID = 1:12,
  cohort = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L)
)

# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  sigma2betaW = 0.01,
  rw1 = TRUE
)

# Sample from the posterior distribution.
my_options <- McmcOptions(
  burnin = 50,
  step = 2,
  samples = 4,
  rng_kind = "Mersenne-Twister",
  rng_seed = 1
)

samples <- mcmc(data = my_data, model = my_model, options = my_options)
samples
# Create some data from the class `DataDual`.
plcb <- 0.001
my_data <- DataDual(
  w = c(13, 77, 86, 26, 27, 36, 37, 97, 21, 49, 87, 48),
  x = c(plcb, 25, 25, 25, plcb, 50, 50, 50, plcb, 100, 100, 100),
  y = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L),
  doseGrid = c(plcb, seq(25, 300, 25)),
  placebo = TRUE,
  ID = 1:12,
  cohort = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L)
)

# Initialize the CRM model.
my_model <- DualEndpointBeta(
  mean = c(0, 1),
  cov = diag(2),
  ref_dose = 2,
  use_log_dose = FALSE,
  sigma2W = c(a = 1, b = 2),
  rho = c(a = 1.5, b = 2.5),
  E0 = 2,
  Emax = 50,
  delta1 = 6,
  mode = 9,
  ref_dose_beta = my_data@doseGrid[my_data@nGrid] + 10
)

# Sample from the posterior distribution.
my_options <- McmcOptions(
  burnin = 50,
  step = 2,
  samples = 4,
  rng_kind = "Mersenne-Twister",
  rng_seed = 1
)

samples <- mcmc(data = my_data, model = my_model, options = my_options)
samples
##obtain mcmc DLE samples given the data, LogisticIndepBeta (DLE model) and mcmc simulations options
## data must be of 'Data' class
data <- Data(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(25, 300, 25)
)
## model must be of 'LogisticIndepBeta' class
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## options must be ''McmcOptions' class
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
set.seed(94)
samples <- mcmc(data = data, model = model, options = options)
# nolint start
##obtain mcmc efficacy samples given the data, 'Effloglog' model (efficacy model) and
## mcmc simulations options data must be of 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## model must be of 'Effloglog' class
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)

## options must be ''McmcOptions' class
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
set.seed(94)
samples <- mcmc(data = data, model = Effmodel, options = options)
# nolint end
## obtain mcmc efficacy samples given the data, 'EffFlexi' model (efficacy model) and
## mcmc simulations options
## data must be of 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25)
)
## model must be of 'EffFlexi' class

effmodel <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)

## options must be ''McmcOptions' class
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
set.seed(94)
samples <- mcmc(data = data, model = effmodel, options = options)
ordinal_data <- .DefaultDataOrdinal()
ordinal_model <- .DefaultLogisticLogNormalOrdinal()
mcmc_options <- .DefaultMcmcOptions()

samples <- mcmc(ordinal_data, ordinal_model, mcmc_options)

`McmcOptions`

Description

McmcOptions is a class for the three canonical MCMC options as well as Random Number Generator settings.

Usage

McmcOptions(
  burnin = 10000L,
  step = 2L,
  samples = 10000L,
  rng_kind = NA_character_,
  rng_seed = NA_integer_
)

.DefaultMcmcOptions()
McmcOptions(
  burnin = 10000L,
  step = 2L,
  samples = 10000L,
  rng_kind = NA_character_,
  rng_seed = NA_integer_
)

.DefaultMcmcOptions()

Arguments

burnin

(count)
number of burn-in iterations which are not saved.

step

(count)
only every step-th iteration is saved after the burn-in.

samples

(count)
number of resulting samples.

rng_kind

(string)
the name of the RNG type. Possible types are: Wichmann-Hill, Marsaglia-Multicarry, Super-Duper, Mersenne-Twister. If it is NA (default), then the RNG kind will be chosen by ⁠[rjags⁠].

rng_seed

(number)
RNG seed corresponding to chosen rng_kind. It must be an integer value or NA (default), which means that the seed will be chosen by ⁠[rjags⁠].

Slots

iterations: (count)
number of MCMC iterations.
burnin: (count)
number of burn-in iterations which are not saved.
step: (count)
only every step-th iteration is saved after the burnin. In other words, a sample from iteration ⁠i = 1,...,iterations⁠, is saved if and only if ⁠(i - burnin) mod step = 0⁠.
For example, for iterations = 6, burnin = 0 and step = 2, only samples from iterations ⁠2,4,6⁠ will be saved.
rng_kind: (string)
a Random Number Generator (RNG) type used by rjags::rjags. It must be one out of the following four values: base::Wichmann-Hill, base::Marsaglia-Multicarry, base::Super-Duper, base::Mersenne-Twister, or NA_character_. If it is NA_character_ (default), then the RNG kind will be chosen by rjags::rjags.
rng_seed: (number)
a Random Number Generator (RNG) seed used by rjags::rjags for a chosen rng_kind. It must be an integer scalar or NA_integer_, which means that the seed will be chosen by rjags::rjags.

Note

Typically, end users will not use the .DefaultMcmcOptions() function.

Examples

# Set up MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples.
McmcOptions(burnin = 10000, step = 2, samples = 10000)
# Set up MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples.
McmcOptions(burnin = 10000, step = 2, samples = 10000)

Construct a Minimally Informative Prior

Description

This function constructs a minimally informative prior, which is captured in a LogisticNormal (or LogisticLogNormal) object.

Based on the proposal by Neuenschwander et al. (2008), a minimally informative prior distribution is constructed. The required key input is the minimum ( $d_{1}$ in the notation of the Appendix A.1 of that paper) and the maximum value ( $d_{J}$ ) of the dose grid supplied to this function. Then threshmin is the probability threshold $q_{1}$ , such that any probability of DLT larger than $q_{1}$ has only 5% probability. Therefore $q_{1}$ is the 95% quantile of the beta distribution and hence $p_{1} = 0.95$ . Likewise, threshmax is the probability threshold $q_{J}$ , such that any probability of DLT smaller than $q_{J}$ has only 5% probability ( $p_{J} = 0.05$ ). The probabilities $1 - p_{1}$ and $p_{J}$ can be controlled with the arguments probmin and probmax, respectively. Subsequently, for all doses supplied in the dosegrid argument, beta distributions are set up from the assumption that the prior medians are linear in log-dose on the logit scale, and Quantiles2LogisticNormal() is used to transform the resulting quantiles into an approximating LogisticNormal (or LogisticLogNormal) model. Note that the reference dose is not required for these computations.

Usage

MinimalInformative(
  dosegrid,
  refDose,
  threshmin = 0.2,
  threshmax = 0.3,
  probmin = 0.05,
  probmax = 0.05,
  ...
)
MinimalInformative(
  dosegrid,
  refDose,
  threshmin = 0.2,
  threshmax = 0.3,
  probmin = 0.05,
  probmax = 0.05,
  ...
)

Arguments

dosegrid

(numeric)
the dose grid.

refDose

(number)
the reference dose.

threshmin

(number)
any toxicity probability above this threshold would be very unlikely (see probmin) at the minimum dose.

threshmax

(number)
any toxicity probability below this threshold would be very unlikely (see probmax) at the maximum dose.

probmin

(number)
the prior probability of exceeding threshmin at the minimum dose.

probmax

(number)
the prior probability of being below threshmax at the maximum dose.

...

additional arguments for computations, see Quantiles2LogisticNormal(), e.g. refDose and logNormal=TRUE to obtain a minimal informative log normal prior.

Value

See Quantiles2LogisticNormal().

References

Neuenschwander B, Branson M, Gsponer T (2008). “Critical aspects of the Bayesian approach to phase I cancer trials.” Statistics in Medicine, 27(13), 2420–2439. https://onlinelibrary.wiley.com/doi/10.1002/sim.3230.

Examples


# Setting up a minimal informative prior 
# max.time is quite small only for the purpose of showing the example. They 
# should be increased for a real case.
set.seed(132)
coarseGrid <- c(0.1, 10, 30, 60, 100)
minInfModel <- MinimalInformative(dosegrid = coarseGrid,
                                  refDose=50,
                                  threshmin=0.2,
                                  threshmax=0.3,
                                  control=## for real case: leave out control 
                                    list(max.time=0.1)) 

# Plotting the result
matplot(x=coarseGrid,
        y=minInfModel$required,
        type="b", pch=19, col="blue", lty=1,
        xlab="dose",
        ylab="prior probability of DLT")
matlines(x=coarseGrid,
         y=minInfModel$quantiles,
         type="b", pch=19, col="red", lty=1)
legend("right",
       legend=c("quantiles", "approximation"),
       col=c("blue", "red"),
       lty=1,
       bty="n")

# Setting up a minimal informative prior 
# max.time is quite small only for the purpose of showing the example. They 
# should be increased for a real case.
set.seed(132)
coarseGrid <- c(0.1, 10, 30, 60, 100)
minInfModel <- MinimalInformative(dosegrid = coarseGrid,
                                  refDose=50,
                                  threshmin=0.2,
                                  threshmax=0.3,
                                  control=## for real case: leave out control 
                                    list(max.time=0.1)) 

# Plotting the result
matplot(x=coarseGrid,
        y=minInfModel$required,
        type="b", pch=19, col="blue", lty=1,
        xlab="dose",
        ylab="prior probability of DLT")
matlines(x=coarseGrid,
         y=minInfModel$quantiles,
         type="b", pch=19, col="red", lty=1)
legend("right",
       legend=c("quantiles", "approximation"),
       col=c("blue", "red"),
       lty=1,
       bty="n")

"MIN" Combination of Cohort Size Rules

Description

This function combines cohort size rules by taking the minimum of all sizes.

Usage

minSize(...)

## S4 method for signature 'CohortSize'
minSize(...)
minSize(...)

## S4 method for signature 'CohortSize'
minSize(...)

Arguments

...

Objects of class CohortSize.

Value

The combination as an object of class CohortSizeMin.

Functions

minSize(CohortSize): The method combining cohort size rules by taking minimum.

Examples

# Here is the rule for:
#   having cohort of size 1 for doses <30
#   and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 30), cohort_size = c(1, 3))

# Here is the rule for:
#   having cohort of size 1 until no DLT were observed
#   and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))

# This is combining the two rules above by taking the minimum of the sample sizes of
# the single rules
mySize <- minSize(mySize1, mySize2)
# Here is the rule for:
#   having cohort of size 1 for doses <30
#   and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 30), cohort_size = c(1, 3))

# Here is the rule for:
#   having cohort of size 1 until no DLT were observed
#   and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))

# This is combining the two rules above by taking the minimum of the sample sizes of
# the single rules
mySize <- minSize(mySize1, mySize2)

`ModelEff`

Description

ModelEff is the parent class for efficacy models using pseudo data prior. It is dedicated all efficacy models that have their prior specified in the form of pseudo data (as if there is some data before the trial starts).

The data must obey the convention of the DataDual class. This refers to any observed efficacy/biomarker responses (w in DataDual), the dose levels at which these responses are observed (x in DataDual), all dose levels considered in the study (doseGrid in DataDual), and finally other specifications in DataDual class that can be used to generate prior or posterior modal estimates or samples estimates for model parameter(s). If no responses are observed, at least doseGrid has to be specified in data for which prior modal estimates or samples can be obtained for model parameters based on the specified pseudo data.

Usage

.DefaultModelEff()
.DefaultModelEff()

Slots

data: (DataDual)
observed data that is used to obtain model parameters estimates or samples (see details above).

Note

Typically, end users will not use the .DefaultModelEff() function.

`ModelLogNormal`

Description

ModelLogNormal is the class for a model with a reference dose and bivariate normal prior on the model parameters alpha0 and natural logarithm of alpha1, i.e.:

$(alpha0, log(alpha1)) ~ Normal(mean, cov),$

. Transformations other than log, e.g. identity, can be specified too in priormodel slot. The parameter alpha1 has a log-normal distribution by default to ensure positivity of alpha1 which further guarantees exp(alpha1) > 1. The slots of this class contain the mean vector, the covariance and precision matrices of the bivariate normal distribution, as well as the reference dose. Note that the precision matrix is an inverse of the covariance matrix in the JAGS. All ("normal") model specific classes inherit from this class.

Usage

ModelLogNormal(mean, cov, ref_dose = 1)

.DefaultModelLogNormal()
ModelLogNormal(mean, cov, ref_dose = 1)

.DefaultModelLogNormal()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose $x*$ (strictly positive number).

Slots

params: (ModelParamsNormal)
bivariate normal prior parameters.
ref_dose: (positive_number)
the reference dose.

Note

Typically, end users will not use the .DefaultModelLogNormal() function.

`ModelParamsNormal`

Description

ModelParamsNormal is the class for a bivariate normal model parameters, i.e. the mean vector, covariance matrix and precision matrix. The precision matrix is an inverse of the covariance matrix in the JAGS and it is computed internally by the object constructor function.

Usage

ModelParamsNormal(mean, cov)

.DefaultModelParamsNormal()
ModelParamsNormal(mean, cov)

.DefaultModelParamsNormal()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

Slots

mean: (numeric)
the mean vector.
cov: (matrix)
the covariance matrix.
prec: (matrix)
the precision matrix, which is an inverse matrix of the cov.

Note

Typically, end users will not use the .ModelPAramsNormal() function.

Examples

ModelParamsNormal(mean = c(1, 6), cov = diag(2))
ModelParamsNormal(mean = c(1, 6), cov = diag(2))

`ModelPseudo`

Description

ModelPseudo is the parent class for models that express their prior in the form of pseudo data (as if there is some data before the trial starts).

Usage

.DefaultModelPseudo()
.DefaultModelPseudo()

Note

Typically, end users will not use the .DefaultModelPseudo() function.

`ModelTox`

Description

ModelTox is the parent class for DLE (dose-limiting events) models using pseudo data prior. It is dedicated for DLE models or toxicity models that have their prior specified in the form of pseudo data (as if there is some data before the trial starts).

The data must obey the convention of the Data class. This refers to any observed DLE responses (y in Data), the dose levels (x in Data) at which these responses are observed, all dose levels considered in the study (doseGrid in Data), and finally other specifications in Data class that can be used to generate prior or posterior modal estimates or samples estimates for model parameter(s). If no responses are observed, at least doseGrid has to be specified in data for which prior modal estimates or samples can be obtained for model parameters based on the specified pseudo data.

Usage

.DefaultModelTox()
.DefaultModelTox()

Slots

data: (Data)
observed data that is used to obtain model parameters estimates or samples (see details above).

Note

Typically, end users will not use the .DefaultModelTox() function.

The Names of the Sampled Parameters

Description

A method that returns names of the parameters that are sampled.

Usage

## S4 method for signature 'Samples'
names(x)
## S4 method for signature 'Samples'
names(x)

Arguments

x

(Samples)
object with samples.

Examples

my_samples <- Samples(
  data = list(alpha = 1:5, beta = 15:19),
  options = McmcOptions(burnin = 2, step = 2, samples = 5)
)

names(my_samples)
my_samples <- Samples(
  data = list(alpha = 1:5, beta = 15:19),
  options = McmcOptions(burnin = 2, step = 2, samples = 5)
)

names(my_samples)

Finding the Next Best Dose

Description

A function that computes the recommended next best dose based on the corresponding rule nextBest, the posterior samples from the model and the underlying data.

Usage

nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestEWOC,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 'NextBestMTD,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 'NextBestNCRM,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 
## 'NextBestNCRM,numeric,Samples,GeneralModel,DataParts'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 'NextBestNCRMLoss,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 
## 'NextBestThreePlusThree,missing,missing,missing,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 
## 'NextBestDualEndpoint,numeric,Samples,DualEndpoint,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 'NextBestMinDist,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 
## 'NextBestInfTheory,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 'NextBestTD,numeric,missing,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit = Inf, model, data, in_sim = FALSE, ...)

## S4 method for signature 
## 'NextBestTDsamples,numeric,Samples,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, in_sim, ...)

## S4 method for signature 'NextBestMaxGain,numeric,missing,ModelTox,DataDual'
nextBest(
  nextBest,
  doselimit = Inf,
  model,
  data,
  model_eff,
  in_sim = FALSE,
  ...
)

## S4 method for signature 
## 'NextBestMaxGainSamples,numeric,Samples,ModelTox,DataDual'
nextBest(
  nextBest,
  doselimit = Inf,
  samples,
  model,
  data,
  model_eff,
  samples_eff,
  in_sim = FALSE,
  ...
)

## S4 method for signature 
## 'NextBestProbMTDLTE,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 
## 'NextBestProbMTDMinDist,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestOrdinal,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 
## 'NextBestOrdinal,numeric,Samples,LogisticLogNormalOrdinal,DataOrdinal'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestEWOC,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 'NextBestMTD,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 'NextBestNCRM,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 
## 'NextBestNCRM,numeric,Samples,GeneralModel,DataParts'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 'NextBestNCRMLoss,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 
## 'NextBestThreePlusThree,missing,missing,missing,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 
## 'NextBestDualEndpoint,numeric,Samples,DualEndpoint,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 'NextBestMinDist,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 
## 'NextBestInfTheory,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 'NextBestTD,numeric,missing,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit = Inf, model, data, in_sim = FALSE, ...)

## S4 method for signature 
## 'NextBestTDsamples,numeric,Samples,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, in_sim, ...)

## S4 method for signature 'NextBestMaxGain,numeric,missing,ModelTox,DataDual'
nextBest(
  nextBest,
  doselimit = Inf,
  model,
  data,
  model_eff,
  in_sim = FALSE,
  ...
)

## S4 method for signature 
## 'NextBestMaxGainSamples,numeric,Samples,ModelTox,DataDual'
nextBest(
  nextBest,
  doselimit = Inf,
  samples,
  model,
  data,
  model_eff,
  samples_eff,
  in_sim = FALSE,
  ...
)

## S4 method for signature 
## 'NextBestProbMTDLTE,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 
## 'NextBestProbMTDMinDist,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestOrdinal,numeric,Samples,GeneralModel,Data'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

## S4 method for signature 
## 'NextBestOrdinal,numeric,Samples,LogisticLogNormalOrdinal,DataOrdinal'
nextBest(nextBest, doselimit = Inf, samples, model, data, ...)

Arguments

nextBest

(NextBest)
the rule for the next best dose.

doselimit

(number)
the maximum allowed next dose. If it is an infinity (default), then essentially no dose limit will be applied in the course of dose recommendation calculation.

samples

(Samples)
posterior samples from model parameters given data.

model

(ModelTox)
the DLT model.

data

(Data)
data that was used to generate the samples.

...

additional arguments without method dispatch.

in_sim

(flag)
is this method used in simulations? Default as FALSE. If this flag is TRUE and target dose estimates (during trial and end-of-trial) are outside of the dose grid range, the information message is printed by this method.

model_eff

(Effloglog or EffFlexi)
the efficacy model.

samples_eff

(Samples)
posterior samples from model_eff parameters given data.

Value

A list with the next best dose recommendation (element named value) from the grid defined in data, and a plot depicting this recommendation (element named plot). In case of multiple plots also an element named singlePlots is included. The singlePlots is itself a list with single plots. An additional list with elements describing the outcome of the rule can be contained too.

Functions

nextBest( nextBest = NextBestEWOC, doselimit = numeric, samples = Samples, model = GeneralModel, data = Data ): find the next best dose based on the EWOC rule.
nextBest( nextBest = NextBestMTD, doselimit = numeric, samples = Samples, model = GeneralModel, data = Data ): find the next best dose based on the MTD rule.
nextBest( nextBest = NextBestNCRM, doselimit = numeric, samples = Samples, model = GeneralModel, data = Data ): find the next best dose based on the NCRM method. The additional element probs in the output's list contains the target and overdosing probabilities (across all doses in the dose grid) used in the derivation of the next best dose.
nextBest( nextBest = NextBestNCRM, doselimit = numeric, samples = Samples, model = GeneralModel, data = DataParts ): find the next best dose based on the NCRM method when two parts trial is used.
nextBest( nextBest = NextBestNCRMLoss, doselimit = numeric, samples = Samples, model = GeneralModel, data = Data ): find the next best dose based on the NCRM method and loss function.
nextBest( nextBest = NextBestThreePlusThree, doselimit = missing, samples = missing, model = missing, data = Data ): find the next best dose based on the 3+3 method.
nextBest( nextBest = NextBestDualEndpoint, doselimit = numeric, samples = Samples, model = DualEndpoint, data = Data ): find the next best dose based on the dual endpoint model. The additional list element probs contains the target and overdosing probabilities (across all doses in the dose grid) used in the derivation of the next best dose.
nextBest( nextBest = NextBestMinDist, doselimit = numeric, samples = Samples, model = GeneralModel, data = Data ): gives the dose which is below the dose limit and has an estimated DLT probability which is closest to the target dose.
nextBest( nextBest = NextBestInfTheory, doselimit = numeric, samples = Samples, model = GeneralModel, data = Data ): gives the appropriate dose within an information theoretic framework.
nextBest( nextBest = NextBestTD, doselimit = numeric, samples = missing, model = LogisticIndepBeta, data = Data ): find the next best dose based only on the DLT responses and for LogisticIndepBeta model class object without DLT samples.
nextBest( nextBest = NextBestTDsamples, doselimit = numeric, samples = Samples, model = LogisticIndepBeta, data = Data ): find the next best dose based only on the DLT responses and for LogisticIndepBeta model class object involving DLT samples.
nextBest( nextBest = NextBestMaxGain, doselimit = numeric, samples = missing, model = ModelTox, data = DataDual ): find the next best dose based only on pseudo DLT model ModelTox and Effloglog efficacy model without samples.
nextBest( nextBest = NextBestMaxGainSamples, doselimit = numeric, samples = Samples, model = ModelTox, data = DataDual ): find the next best dose based on DLT and efficacy responses with DLT and efficacy samples.
nextBest( nextBest = NextBestProbMTDLTE, doselimit = numeric, samples = Samples, model = GeneralModel, data = Data ): find the next best dose based with the highest probability of having a toxicity rate less or equal to the target toxicity level.
nextBest( nextBest = NextBestProbMTDMinDist, doselimit = numeric, samples = Samples, model = GeneralModel, data = Data ): find the next best dose based with the highest probability of having a toxicity rate with minimum distance to the target toxicity level.
nextBest( nextBest = NextBestOrdinal, doselimit = numeric, samples = Samples, model = GeneralModel, data = Data ): find the next best dose for ordinal CRM models.
nextBest( nextBest = NextBestOrdinal, doselimit = numeric, samples = Samples, model = LogisticLogNormalOrdinal, data = DataOrdinal ): find the next best dose for ordinal CRM models.

Examples

# Example of usage for `NextBestEWOC` NextBest class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestEWOC' class.
ewoc_next_best <- NextBestEWOC(
  target = 0.30,
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = ewoc_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# See the probabilities.
dose_recommendation$probs
# Example of usage for `NextBestMTD` NextBest class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestMTD' class.
mtd_next_best <- NextBestMTD(
  target = 0.33,
  derive = function(mtd_samples) {
    quantile(mtd_samples, probs = 0.25)
  }
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = mtd_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)
# Example of usage for `NextBestNCRM` NextBest class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestNCRM' class.
nrcm_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = nrcm_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# See the probabilities.
dose_recommendation$probs
# Example of usage for `NextBestNCRM-DataParts` NextBest class.

# Create the data.
my_data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  ID = 1:3,
  cohort = 1:3,
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelativeParts(
  dlt_start = 0,
  clean_start = 1
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestNCRM' class.
nrcm_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = nrcm_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

dose_recommendation
# Example of usage for `NextBestNCRMLoss` NextBest class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the class `NextBestNCRMLoss`.
nrcm_loss_next_best <- NextBestNCRMLoss(
  target = c(0.2, 0.35),
  overdose = c(0.35, 0.6),
  unacceptable = c(0.6, 1),
  max_overdose_prob = 0.999,
  losses = c(1, 0, 1, 2)
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = nrcm_loss_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# Next best dose.
dose_recommendation$value

# Look at the probabilities.
dose_recommendation$probs

# Define another rule (loss function of 3 elements).
nrcm_loss_next_best_losses_3 <- NextBestNCRMLoss(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.30,
  losses = c(1, 0, 2)
)

# Calculate the next best dose.
dose_recommendation_losses_3 <- nextBest(
  nextBest = nrcm_loss_next_best_losses_3,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# Next best dose.
dose_recommendation_losses_3$value

# Look at the probabilities.
dose_recommendation_losses_3$probs
# Example of usage for `NextBestThreePlusThree` NextBest class.

# Create the data.
my_data <- Data(
  x = c(5, 5, 5, 10, 10, 10),
  y = c(0, 0, 0, 0, 1, 0),
  ID = 1:6,
  cohort = c(0, 0, 0, 1, 1, 1),
  doseGrid = c(0.1, 0.5, 1.5, 3, 5, seq(from = 10, to = 80, by = 2))
)

# The rule to select the next best dose will be based on the 3+3 method.
my_next_best <- NextBestThreePlusThree()

# Calculate the next best dose.
dose_recommendation <- nextBest(my_next_best, data = my_data)
# Example of usage for `NextBestDualEndpoint` NextBest class.

# Create the data.
my_data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  ID = 1:17,
  cohort = c(
    1L,
    2L,
    3L,
    4L,
    5L,
    6L,
    6L,
    6L,
    7L,
    7L,
    7L,
    8L,
    8L,
    8L,
    9L,
    9L,
    9L
  ),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1).
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)


my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose. In this case,
# target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety).
de_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = de_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# See the probabilities.
dose_recommendation$probs

# Joint plot.
print(dose_recommendation$plot)

# Show customization of single plot.
variant1 <- dose_recommendation$singlePlots$plot1 + xlim(0, 20)
print(variant1)

# Example of usage for `NextBestTD` NextBest class.
my_data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  ID = 1:8,
  cohort = c(1L, 2L, 2L, 3L, 4L, 5L, 6L, 7L),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = my_data
)

# Target probabilities of the occurrence of a DLT during trial and
# at the end of the trial are defined as 0.35 and 0.3, respectively.
td_next_best <- NextBestTD(prob_target_drt = 0.35, prob_target_eot = 0.3)

# doselimit is the maximum allowable dose level to be given to subjects.
dose_recommendation <- nextBest(
  nextBest = td_next_best,
  doselimit = max(my_data@doseGrid),
  model = my_model,
  data = my_data
)

dose_recommendation$next_dose_drt
dose_recommendation$plot
# Example of usage for `NextBestTDsamples` NextBest class.
my_data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  ID = 1:8,
  cohort = c(1L, 2L, 2L, 3L, 4L, 5L, 6L, 7L),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = my_data
)

# Set-up some MCMC parameters and generate samples.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 800)
my_samples <- mcmc(my_data, my_model, my_options)

# Target probabilities of the occurrence of a DLT during trial and
# at the end of the trial are defined as 0.35 and 0.3, respectively.
# 'derive' is specified such that the 30% posterior quantile of the TD35 and
# TD30 samples will be used as TD35 and TD30 estimates.
tds_next_best <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

# doselimit is the maximum allowable dose level to be given to subjects.
dose_recommendation <- nextBest(
  nextBest = tds_next_best,
  doselimit = max(my_data@doseGrid),
  samples = my_samples,
  model = my_model,
  data = my_data
)

dose_recommendation$next_dose_drt
dose_recommendation$plot
# Example of usage for `NextBestMaxGain` NextBest class.

# Create the data.
my_data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  ID = 1:8,
  cohort = 1:8,
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)

# 'ModelTox' DLT model, e.g 'LogisticIndepBeta'.
my_model_dlt <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = my_data
)

# 'ModelEff' efficacy model, e.g. 'Effloglog'.
my_model_eff <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data
)

# Target probabilities of the occurrence of a DLT during trial and at the
# end of trial are defined as 0.35 and 0.3, respectively.
mg_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# doselimit is the maximum allowable dose level to be given to subjects.
dose_recommendation <- nextBest(
  nextBest = mg_next_best,
  doselimit = 300,
  model = my_model_dlt,
  model_eff = my_model_eff,
  data = my_data
)

dose_recommendation$next_dose
dose_recommendation$plot
# Example of usage for `NextBestMaxGainSamples` NextBest class.

# Create the data.
my_data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  ID = 1:8,
  cohort = 1:8,
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)

# 'ModelTox' DLT model, e.g 'LogisticIndepBeta'.
my_model_dlt <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = my_data
)

# 'ModelEff' efficacy model, e.g 'Effloglog'.
my_model_effll <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples_dlt <- mcmc(my_data, my_model_dlt, my_options)
my_samples_effll <- mcmc(my_data, my_model_effll, my_options)

# Target probabilities of the occurrence of a DLT during trial and at the end of
# trial are defined as 0.35 and 0.3, respectively.
# Use 30% posterior quantile of the TD35 and TD30 samples as estimates of TD35
# and TD30.
# Use 50% posterior quantile of the Gstar (the dose which gives the maxim gain value)
# samples as Gstar estimate.
mgs_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

dose_recommendation <- nextBest(
  nextBest = mgs_next_best,
  doselimit = max(my_data@doseGrid),
  samples = my_samples_dlt,
  model = my_model_dlt,
  data = my_data,
  model_eff = my_model_effll,
  samples_eff = my_samples_effll
)

dose_recommendation$next_dose
dose_recommendation$plot

# Now using the 'EffFlexi' class efficacy model:

my_model_effflexi <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = my_data
)


my_samples_effflexi <- mcmc(my_data, my_model_effflexi, my_options)

dose_recommendation <- nextBest(
  nextBest = mgs_next_best,
  doselimit = max(my_data@doseGrid),
  samples = my_samples_dlt,
  model = my_model_dlt,
  data = my_data,
  model_eff = my_model_effflexi,
  samples_eff = my_samples_effflexi
)

dose_recommendation$next_dose
dose_recommendation$plot

# Example of usage for `NextBestProbMTDLTE` NextBest class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestProbMTDLTE' class.
nb_mtd_lte <- NextBestProbMTDLTE(target = 0.33)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = nb_mtd_lte,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)
# Example of usage for `NextBestProbMTDMinDist` NextBest class.

# Create the data.
my_data <- Data(
  x = c(1.5, 1.5, 1.5, 2.5, 2.5, 2.5, 3.5, 3.5, 3.5),
  y = c(0, 0, 0, 0, 0, 0, 1, 1, 0),
  ID = 1:9,
  cohort = c(1, 1, 1, 2, 2, 2, 3, 3, 3),
  doseGrid = c(1.5, 2.5, 3.5, 4.5, 6, 7)
)

# Initialize the CRM model used to model the data.
my_model <- my_model <- LogisticKadaneBetaGamma(
  theta = 0.3,
  xmin = 1.5,
  xmax = 7,
  alpha = 1,
  beta = 19,
  shape = 0.5625,
  rate = 0.125
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsDoseLevels(levels = 1)

next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestProbMTDMinDist' class.
nb_mtd_min_dist <- NextBestProbMTDMinDist(target = 0.3)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = nb_mtd_min_dist,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)
ordinal_data <- .DefaultDataOrdinal()
ordinal_model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()


ordinal_samples <- mcmc(ordinal_data, ordinal_model, options)

nextBest(
  nextBest = NextBestOrdinal(2L, .DefaultNextBestNCRM()),
  samples = ordinal_samples,
  doselimit = Inf,
  model = ordinal_model,
  data = ordinal_data
)

ordinal_data <- .DefaultDataOrdinal()
ordinal_model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()


ordinal_samples <- mcmc(ordinal_data, ordinal_model, options)

nextBest(
  nextBest = NextBestOrdinal(2L, .DefaultNextBestNCRM()),
  samples = ordinal_samples,
  doselimit = Inf,
  model = ordinal_model,
  data = ordinal_data
)

# Example of usage for `NextBestEWOC` NextBest class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestEWOC' class.
ewoc_next_best <- NextBestEWOC(
  target = 0.30,
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = ewoc_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# See the probabilities.
dose_recommendation$probs
# Example of usage for `NextBestMTD` NextBest class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestMTD' class.
mtd_next_best <- NextBestMTD(
  target = 0.33,
  derive = function(mtd_samples) {
    quantile(mtd_samples, probs = 0.25)
  }
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = mtd_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)
# Example of usage for `NextBestNCRM` NextBest class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestNCRM' class.
nrcm_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = nrcm_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# See the probabilities.
dose_recommendation$probs
# Example of usage for `NextBestNCRM-DataParts` NextBest class.

# Create the data.
my_data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  ID = 1:3,
  cohort = 1:3,
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelativeParts(
  dlt_start = 0,
  clean_start = 1
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestNCRM' class.
nrcm_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = nrcm_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

dose_recommendation
# Example of usage for `NextBestNCRMLoss` NextBest class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the class `NextBestNCRMLoss`.
nrcm_loss_next_best <- NextBestNCRMLoss(
  target = c(0.2, 0.35),
  overdose = c(0.35, 0.6),
  unacceptable = c(0.6, 1),
  max_overdose_prob = 0.999,
  losses = c(1, 0, 1, 2)
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = nrcm_loss_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# Next best dose.
dose_recommendation$value

# Look at the probabilities.
dose_recommendation$probs

# Define another rule (loss function of 3 elements).
nrcm_loss_next_best_losses_3 <- NextBestNCRMLoss(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.30,
  losses = c(1, 0, 2)
)

# Calculate the next best dose.
dose_recommendation_losses_3 <- nextBest(
  nextBest = nrcm_loss_next_best_losses_3,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# Next best dose.
dose_recommendation_losses_3$value

# Look at the probabilities.
dose_recommendation_losses_3$probs
# Example of usage for `NextBestThreePlusThree` NextBest class.

# Create the data.
my_data <- Data(
  x = c(5, 5, 5, 10, 10, 10),
  y = c(0, 0, 0, 0, 1, 0),
  ID = 1:6,
  cohort = c(0, 0, 0, 1, 1, 1),
  doseGrid = c(0.1, 0.5, 1.5, 3, 5, seq(from = 10, to = 80, by = 2))
)

# The rule to select the next best dose will be based on the 3+3 method.
my_next_best <- NextBestThreePlusThree()

# Calculate the next best dose.
dose_recommendation <- nextBest(my_next_best, data = my_data)
# Example of usage for `NextBestDualEndpoint` NextBest class.

# Create the data.
my_data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  ID = 1:17,
  cohort = c(
    1L,
    2L,
    3L,
    4L,
    5L,
    6L,
    6L,
    6L,
    7L,
    7L,
    7L,
    8L,
    8L,
    8L,
    9L,
    9L,
    9L
  ),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1).
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)


my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose. In this case,
# target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety).
de_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = de_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# See the probabilities.
dose_recommendation$probs

# Joint plot.
print(dose_recommendation$plot)

# Show customization of single plot.
variant1 <- dose_recommendation$singlePlots$plot1 + xlim(0, 20)
print(variant1)

# Example of usage for `NextBestTD` NextBest class.
my_data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  ID = 1:8,
  cohort = c(1L, 2L, 2L, 3L, 4L, 5L, 6L, 7L),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = my_data
)

# Target probabilities of the occurrence of a DLT during trial and
# at the end of the trial are defined as 0.35 and 0.3, respectively.
td_next_best <- NextBestTD(prob_target_drt = 0.35, prob_target_eot = 0.3)

# doselimit is the maximum allowable dose level to be given to subjects.
dose_recommendation <- nextBest(
  nextBest = td_next_best,
  doselimit = max(my_data@doseGrid),
  model = my_model,
  data = my_data
)

dose_recommendation$next_dose_drt
dose_recommendation$plot
# Example of usage for `NextBestTDsamples` NextBest class.
my_data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  ID = 1:8,
  cohort = c(1L, 2L, 2L, 3L, 4L, 5L, 6L, 7L),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = my_data
)

# Set-up some MCMC parameters and generate samples.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 800)
my_samples <- mcmc(my_data, my_model, my_options)

# Target probabilities of the occurrence of a DLT during trial and
# at the end of the trial are defined as 0.35 and 0.3, respectively.
# 'derive' is specified such that the 30% posterior quantile of the TD35 and
# TD30 samples will be used as TD35 and TD30 estimates.
tds_next_best <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

# doselimit is the maximum allowable dose level to be given to subjects.
dose_recommendation <- nextBest(
  nextBest = tds_next_best,
  doselimit = max(my_data@doseGrid),
  samples = my_samples,
  model = my_model,
  data = my_data
)

dose_recommendation$next_dose_drt
dose_recommendation$plot
# Example of usage for `NextBestMaxGain` NextBest class.

# Create the data.
my_data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  ID = 1:8,
  cohort = 1:8,
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)

# 'ModelTox' DLT model, e.g 'LogisticIndepBeta'.
my_model_dlt <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = my_data
)

# 'ModelEff' efficacy model, e.g. 'Effloglog'.
my_model_eff <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data
)

# Target probabilities of the occurrence of a DLT during trial and at the
# end of trial are defined as 0.35 and 0.3, respectively.
mg_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# doselimit is the maximum allowable dose level to be given to subjects.
dose_recommendation <- nextBest(
  nextBest = mg_next_best,
  doselimit = 300,
  model = my_model_dlt,
  model_eff = my_model_eff,
  data = my_data
)

dose_recommendation$next_dose
dose_recommendation$plot
# Example of usage for `NextBestMaxGainSamples` NextBest class.

# Create the data.
my_data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  ID = 1:8,
  cohort = 1:8,
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)

# 'ModelTox' DLT model, e.g 'LogisticIndepBeta'.
my_model_dlt <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = my_data
)

# 'ModelEff' efficacy model, e.g 'Effloglog'.
my_model_effll <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = my_data
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples_dlt <- mcmc(my_data, my_model_dlt, my_options)
my_samples_effll <- mcmc(my_data, my_model_effll, my_options)

# Target probabilities of the occurrence of a DLT during trial and at the end of
# trial are defined as 0.35 and 0.3, respectively.
# Use 30% posterior quantile of the TD35 and TD30 samples as estimates of TD35
# and TD30.
# Use 50% posterior quantile of the Gstar (the dose which gives the maxim gain value)
# samples as Gstar estimate.
mgs_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

dose_recommendation <- nextBest(
  nextBest = mgs_next_best,
  doselimit = max(my_data@doseGrid),
  samples = my_samples_dlt,
  model = my_model_dlt,
  data = my_data,
  model_eff = my_model_effll,
  samples_eff = my_samples_effll
)

dose_recommendation$next_dose
dose_recommendation$plot

# Now using the 'EffFlexi' class efficacy model:

my_model_effflexi <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = my_data
)


my_samples_effflexi <- mcmc(my_data, my_model_effflexi, my_options)

dose_recommendation <- nextBest(
  nextBest = mgs_next_best,
  doselimit = max(my_data@doseGrid),
  samples = my_samples_dlt,
  model = my_model_dlt,
  data = my_data,
  model_eff = my_model_effflexi,
  samples_eff = my_samples_effflexi
)

dose_recommendation$next_dose
dose_recommendation$plot

# Example of usage for `NextBestProbMTDLTE` NextBest class.

# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestProbMTDLTE' class.
nb_mtd_lte <- NextBestProbMTDLTE(target = 0.33)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = nb_mtd_lte,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)
# Example of usage for `NextBestProbMTDMinDist` NextBest class.

# Create the data.
my_data <- Data(
  x = c(1.5, 1.5, 1.5, 2.5, 2.5, 2.5, 3.5, 3.5, 3.5),
  y = c(0, 0, 0, 0, 0, 0, 1, 1, 0),
  ID = 1:9,
  cohort = c(1, 1, 1, 2, 2, 2, 3, 3, 3),
  doseGrid = c(1.5, 2.5, 3.5, 4.5, 6, 7)
)

# Initialize the CRM model used to model the data.
my_model <- my_model <- LogisticKadaneBetaGamma(
  theta = 0.3,
  xmin = 1.5,
  xmax = 7,
  alpha = 1,
  beta = 19,
  shape = 0.5625,
  rate = 0.125
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsDoseLevels(levels = 1)

next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the 'NextBestProbMTDMinDist' class.
nb_mtd_min_dist <- NextBestProbMTDMinDist(target = 0.3)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  nextBest = nb_mtd_min_dist,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)
ordinal_data <- .DefaultDataOrdinal()
ordinal_model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()


ordinal_samples <- mcmc(ordinal_data, ordinal_model, options)

nextBest(
  nextBest = NextBestOrdinal(2L, .DefaultNextBestNCRM()),
  samples = ordinal_samples,
  doselimit = Inf,
  model = ordinal_model,
  data = ordinal_data
)

ordinal_data <- .DefaultDataOrdinal()
ordinal_model <- .DefaultLogisticLogNormalOrdinal()
options <- .DefaultMcmcOptions()


ordinal_samples <- mcmc(ordinal_data, ordinal_model, options)

nextBest(
  nextBest = NextBestOrdinal(2L, .DefaultNextBestNCRM()),
  samples = ordinal_samples,
  doselimit = Inf,
  model = ordinal_model,
  data = ordinal_data
)

`NextBest`

Description

NextBest is a virtual class for finding next best dose, from which all other specific next best dose classes inherit.

Usage

.DefaultNextBest()
.DefaultNextBest()

Note

Typically, end users will not use the DefaultNextBest() function.

`NextBestDualEndpoint`

Description

NextBestDualEndpoint is the class for next best dose that is based on the dual endpoint model.

Usage

NextBestDualEndpoint(
  target,
  overdose,
  max_overdose_prob,
  target_relative = TRUE,
  target_thresh = 0.01
)

.DefaultNextBestDualEndpoint()
NextBestDualEndpoint(
  target,
  overdose,
  max_overdose_prob,
  target_relative = TRUE,
  target_thresh = 0.01
)

.DefaultNextBestDualEndpoint()

Arguments

target

(numeric)
see slot definition.

overdose

(numeric)
see slot definition.

max_overdose_prob

(proportion)
see slot definition.

target_relative

(flag)
see slot definition.

target_thresh

(proportion)
see slot definition.

Details

Under this rule, at first admissible doses are found, which are those with toxicity probability to fall in overdose category and being below max_overdose_prob. Next, it picks (from the remaining admissible doses) the one that maximizes the probability to be in the target biomarker range. By default (target_relative = TRUE) the target is specified as relative to the maximum biomarker level across the dose grid or relative to the Emax parameter in case a parametric model was selected (i.e. DualEndpointBeta, DualEndpointEmax). However, if target_relative = FALSE, then the absolute biomarker range can be used as a target.

Slots

target: (numeric)
the biomarker target range that needs to be reached. For example, the target range $(0.8, 1.0)$ and target_relative = TRUE means that we target a dose with at least $80\%$ of maximum biomarker level. As an other example, $(0.5, 0.8)$ would mean that we target a dose between $50\%$ and $80\%$ of the maximum biomarker level.
overdose: (numeric)
the overdose toxicity interval (lower limit excluded, upper limit included).
max_overdose_prob: (proportion)
maximum overdose probability that is allowed.
target_relative: (flag)
is target specified as relative? If TRUE, then the target is interpreted relative to the maximum, so it must be a probability range. Otherwise, the target is interpreted as absolute biomarker range.
target_thresh: (proportion)
a target probability threshold that needs to be fulfilled before the target probability will be used for deriving the next best dose (default to 0.01).

Note

Typically, end users will not use the .DefaultNextBestDualEndpoint() function.

Examples

# Target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT) > 0.35 (safety).
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Now, using absolute target on the natural biomarker scale.
my_next_best_absolute <- NextBestDualEndpoint(
  target = c(200, 300),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25,
  target_relative = FALSE
)
# Target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT) > 0.35 (safety).
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Now, using absolute target on the natural biomarker scale.
my_next_best_absolute <- NextBestDualEndpoint(
  target = c(200, 300),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25,
  target_relative = FALSE
)

`NextBestEWOC`

Description

NextBestEWOC is the class implementing Escalation With Overdose Control (EWOC). It recommends the highest possible dose subject to a probabilistic constraint that the posterior probability of overdosing does not exceed max_overdose_prob. Overdosing is defined as the model-based toxicity probability lying inside the interval given by overdose.

Usage

NextBestEWOC(target, overdose, max_overdose_prob)

.DefaultNextBestEWOC()
NextBestEWOC(target, overdose, max_overdose_prob)

.DefaultNextBestEWOC()

Arguments

target

(proportion)
see slot definition.

overdose

(numeric)
see slot definition.

max_overdose_prob

(proportion)
see slot definition.

Slots

target: (proportion)
target toxicity probability to be achieved, below overdose[1]; only used for simulation reporting purposes.
overdose: (numeric)
the (exclusive) lower and (inclusive) upper boundaries of the toxicity probability interval considered an overdose region. The prototype uses c(0.35, 1) meaning probabilities > 0.35 are treated as overly toxic.
max_overdose_prob: (proportion)
maximum acceptable posterior probability that the next recommended dose is in the overdose interval.

Note

Typically, end users will not use the .DefaultNextBestEWOC() function.

Examples

# Example: Define EWOC next best dose rule.
# Target toxicity probability is 0.30. Overdose region is any probability > 0.35.
# We restrict posterior probability of recommending an overdosing dose to <= 0.25.
next_best_ewoc <- NextBestEWOC(
  target = 0.30,
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)
# Example: Define EWOC next best dose rule.
# Target toxicity probability is 0.30. Overdose region is any probability > 0.35.
# We restrict posterior probability of recommending an overdosing dose to <= 0.25.
next_best_ewoc <- NextBestEWOC(
  target = 0.30,
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

`NextBestInfTheory`

Description

NextBestInfTheory is the class for next best dose that is based on information theory as proposed in https://doi.org/10.1002/sim.8450.

Usage

NextBestInfTheory(target, asymmetry)

.DefaultNextBestInfTheory()
NextBestInfTheory(target, asymmetry)

.DefaultNextBestInfTheory()

Arguments

target

(proportion)
see slot definition.

asymmetry

(number)
see slot definition.

Slots

target: (proportion)
target toxicity probability, except 0 or 1.
asymmetry: (number)
value of the asymmetry exponent in the divergence function that describes the rate of penalization for overly toxic does. It must be a value from $(0, 2)$ interval.

Note

Typically, end users will not use the .DefaultNextBestInfTheory() function.

`NextBestMaxGain`

Description

NextBestMaxGain is the class to find a next best dose with maximum gain value based on a pseudo DLT and efficacy models without samples. It is based solely on the probabilities of the occurrence of a DLT and the values of the mean efficacy responses obtained by using the modal estimates of the DLT and efficacy model parameters. There are two target probabilities of the occurrence of a DLT that must be specified: target probability to be used during the trial and target probability to be used at the end of the trial. It is suitable to use it only with the ModelTox model and ModelEff classes (except EffFlexi).

Usage

NextBestMaxGain(prob_target_drt, prob_target_eot)

.DefaultNextBestMaxGain()
NextBestMaxGain(prob_target_drt, prob_target_eot)

.DefaultNextBestMaxGain()

Arguments

prob_target_drt

(proportion)
see slot definition.

prob_target_eot

(proportion)
see slot definition.

Slots

prob_target_drt: (proportion)
the target probability of the occurrence of a DLT to be used during the trial.
prob_target_eot: (proportion)
the target probability of the occurrence of a DLT to be used at the end of the trial.

Note

Typically, end users will not use the .DefaultNextBestMaxGain() function.

Examples

my_next_best <- NextBestMaxGain(0.35, 0.3)
my_next_best <- NextBestMaxGain(0.35, 0.3)

`NextBestMaxGainSamples`

Description

NextBestMaxGainSamples is the class to find a next best dose with maximum gain value based on a pseudo DLT and efficacy models and DLT and efficacy samples. There are two target probabilities of the occurrence of a DLT that must be specified: target probability to be used during the trial and target probability to be used at the end of the trial. It is suitable to use it only with the ModelTox model and ModelEff classes.

Usage

NextBestMaxGainSamples(prob_target_drt, prob_target_eot, derive, mg_derive)

.DefaultNextBestMaxGainSamples()
NextBestMaxGainSamples(prob_target_drt, prob_target_eot, derive, mg_derive)

.DefaultNextBestMaxGainSamples()

Arguments

prob_target_drt

(proportion)
see slot definition in NextBestMaxGain.

prob_target_eot

(proportion)
see slot definition in NextBestMaxGain.

derive

(function)
see slot definition.

mg_derive

(function)
see slot definition.

Slots

derive: (function)
derives, based on a vector of posterior dose samples, the target dose that has the probability of the occurrence of DLT equals to either the prob_target_drt or prob_target_eot. It must therefore accept one and only one argument, which is a numeric vector, and return a number.
mg_derive: (function)
derives, based on a vector of posterior dose samples that give the maximum gain value, the final next best estimate of the dose that gives the maximum gain value. It must therefore accept one and only one argument, which is a numeric vector, and return a number.

Note

Typically, end users will not use the .DefaultNextBestMaxGainSamples() function.

Examples

# Target probability of the occurrence of a DLT during the trial is set to 0.35.
# Target probability of the occurrence of a DLT at the end of the trial is set to 0.3.
# We want the use the 30% posterior quantile (the 30th percentile) of the TD35
# (the dose level with probability of the DLT equals 0.35) and TD30 samples.
# For `mg_derive` function (which takes the sample of doses which give the maximum
# gain), we will use the 50% posterior quantile (the median or th 50th percentile)
# of the sample.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)
# Target probability of the occurrence of a DLT during the trial is set to 0.35.
# Target probability of the occurrence of a DLT at the end of the trial is set to 0.3.
# We want the use the 30% posterior quantile (the 30th percentile) of the TD35
# (the dose level with probability of the DLT equals 0.35) and TD30 samples.
# For `mg_derive` function (which takes the sample of doses which give the maximum
# gain), we will use the 50% posterior quantile (the median or th 50th percentile)
# of the sample.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

`NextBestMinDist`

Description

NextBestMinDist is the class for next best dose that is based on minimum distance to target probability.

Usage

NextBestMinDist(target)

.DefaultNextBestMinDist()
NextBestMinDist(target)

.DefaultNextBestMinDist()

Arguments

target

(proportion)
see slot definition.

Slots

target: (proportion)
single target toxicity probability, except 0 or 1.

Note

Typically, end users will not use the .DefaultNextBestMinDist() function.

Examples

# In the example below, the MTD is defined as the dose with the toxicity rate
# with minimal distance to the target of 30%.
next_best_min_dist <- NextBestMinDist(target = 0.3)
# In the example below, the MTD is defined as the dose with the toxicity rate
# with minimal distance to the target of 30%.
next_best_min_dist <- NextBestMinDist(target = 0.3)

`NextBestMTD`

Description

NextBestMTD is the class for next best dose based on MTD estimate.

Usage

NextBestMTD(target, derive)

.DefaultNextBestMTD()
NextBestMTD(target, derive)

.DefaultNextBestMTD()

Arguments

target

(proportion)
see slot definition.

derive

(function)
see slot definition.

Slots

target: (proportion)
target toxicity probability, except 0 or 1.
derive: (function)
a function which derives the final next best MTD estimate, based on vector of posterior MTD samples. It must therefore accept one and only one argument, which is a numeric vector, and return a number.

Note

Typically, end users will not use the .DefaultNextBestMTD() function.

Examples

# In the example below, the MTD is defined as the dose for which prob(DLE) = 0.33
# and we will use the 25th quantile of the posterior of MTD as our next best dose.
next_best_mtd <- NextBestMTD(
  target = 0.33,
  derive = function(mtd_samples) {
    quantile(mtd_samples, probs = 0.25)
  }
)
# In the example below, the MTD is defined as the dose for which prob(DLE) = 0.33
# and we will use the 25th quantile of the posterior of MTD as our next best dose.
next_best_mtd <- NextBestMTD(
  target = 0.33,
  derive = function(mtd_samples) {
    quantile(mtd_samples, probs = 0.25)
  }
)

`NextBestNCRM`

Description

NextBestNCRM is the class for next best dose that finds the next dose with high posterior probability to be in the target toxicity interval.

Usage

NextBestNCRM(target, overdose, max_overdose_prob)

.DefaultNextBestNCRM()
NextBestNCRM(target, overdose, max_overdose_prob)

.DefaultNextBestNCRM()

Arguments

target

(numeric)
see slot definition.

overdose

(numeric)
see slot definition.

max_overdose_prob

(proportion)
see slot definition.

Details

To avoid numerical problems, the dose selection algorithm has been implemented as follows: First admissible doses are found, which are those with probability to fall in overdose category being below max_overdose_prob. Next, within the admissible doses, the maximum probability to fall in the target category is calculated. If that is above 5% (i.e. it is not just numerical error), then the corresponding dose is the next recommended dose. Otherwise, the highest admissible dose is the next recommended dose.

Slots

target: (numeric)
the target toxicity interval (limits included).
overdose: (numeric)
the overdose toxicity interval (lower limit excluded, upper limit included). It is used to filter probability samples.
max_overdose_prob: (proportion)
maximum overdose posterior probability that is allowed, except 0 or 1.

Note

Typically, end users will not use the .DefaultNextBestNCRM() function.

Examples

# In the example below, the target toxicity interval [0.2, 0.35] while the
# overdose interval is (0.35,1]. Finally we would like to constrain the
# probability of overdosing below 25%.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)
# In the example below, the target toxicity interval [0.2, 0.35] while the
# overdose interval is (0.35,1]. Finally we would like to constrain the
# probability of overdosing below 25%.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

`NextBestNCRMLoss`

Description

NextBestNCRMLoss is the class based on NCRM rule and loss function. This class is similar to NextBestNCRM class, but differences are the addition of loss function and re-defined toxicity intervals, see each toxicity interval documentation and the note for details. As in NCRM rule, first admissible doses are found, which are those with probability to fall in overdose category being below max_overdose_prob. Next, within the admissible doses, the loss function is calculated, i.e. losses %*% target. Finally, the corresponding dose with lowest loss function (Bayes risk) is recommended for the next dose.

Usage

NextBestNCRMLoss(
  target,
  overdose,
  unacceptable = c(1, 1),
  max_overdose_prob,
  losses
)

.DefaultNextBestNCRMLoss()
NextBestNCRMLoss(
  target,
  overdose,
  unacceptable = c(1, 1),
  max_overdose_prob,
  losses
)

.DefaultNextBestNCRMLoss()

Arguments

target

(numeric)
see slot definition.

overdose

(numeric)
see slot definition.

unacceptable

(numeric)
see slot definition.

max_overdose_prob

(proportion)
see slot definition in NextBestNCRM.

losses

(numeric)
see slot definition.

Slots

target: (numeric)
the target toxicity interval (limits included). It has to be a probability range excluding 0 and 1.
overdose: (numeric)
the overdose toxicity interval (lower limit excluded, upper limit included) or the excessive toxicity interval (lower limit excluded, upper limit included) if unacceptable is not provided. It has to be a probability range. It is used to filter probability samples.
unacceptable: (numeric)
an unacceptable toxicity interval (lower limit excluded, upper limit included). This must be specified if the overdose does not include 1. Otherwise, it is c(1, 1) (default), which is essentially a scalar equals 1. It has to be a probability range.
losses: (numeric)
a vector specifying the loss function. If the unacceptable is provided, the vector length must be 4, otherwise 3.

Note

The loss function should be a vector of either 3 or 4 values. This is because the loss function values must be specified for each interval, that is under-dosing, target toxicity, and overdosing toxicity or under-dosing, target toxicity, overdosing (excessive) toxicity, and unacceptable toxicity intervals.

Typically, end users will not use the .DefaultNextBestnCRMLoss() function.

Examples

# In the example below, the target toxicity interval [0.2, 0.35] while the
# overdose interval is (0.35, 1]. We would like to constrain the probability
# of overdosing below 25%. The loss function is c(1, 0, 1, 2).
my_next_best <- NextBestNCRMLoss(
  target = c(0.2, 0.35),
  overdose = c(0.35, 0.6),
  unacceptable = c(0.6, 1),
  max_overdose_prob = 0.25,
  losses = c(1, 0, 1, 2)
)
# In the example below, the target toxicity interval [0.2, 0.35] while the
# overdose interval is (0.35, 1]. We would like to constrain the probability
# of overdosing below 25%. The loss function is c(1, 0, 1, 2).
my_next_best <- NextBestNCRMLoss(
  target = c(0.2, 0.35),
  overdose = c(0.35, 0.6),
  unacceptable = c(0.6, 1),
  max_overdose_prob = 0.25,
  losses = c(1, 0, 1, 2)
)

`NextBestOrdinal`

Description

NextBestOrdinal is the class for applying a standard NextBest rule to the results of an ordinal CRM trial.

Usage

NextBestOrdinal(grade, rule)

.DefaultNextBestOrdinal()
NextBestOrdinal(grade, rule)

.DefaultNextBestOrdinal()

Arguments

grade

(numeric)
see slot definition.

rule

(NextBest)
see slot definition.

Slots

grade: (integer)
the toxicity grade to which the rule should be applied.
rule: (NextBest)
the standard NextBest rule to be applied

Note

Typically, end users will not use the .DefaultNextBestOrdinal() function.

Examples

NextBestOrdinal(
  grade = 1L,
  rule = NextBestMTD(
    0.25,
    function(mtd_samples) {
      quantile(mtd_samples, probs = 0.25)
    }
  )
)
NextBestOrdinal(
  grade = 1L,
  rule = NextBestMTD(
    0.25,
    function(mtd_samples) {
      quantile(mtd_samples, probs = 0.25)
    }
  )
)

`NextBestProbMTDLTE`

Description

NextBestProbMTDLTE is the class of finding a next best dose that selects the dose with the highest probability of having a toxicity rate less or equal to the toxicity target. The dose is determined by calculating the posterior toxicity probability for each dose per iteration and select the maximum dose that has a toxicity probability below or equal to the target. The dose with the highest frequency of being selected as MTD across iterations is the next best dose. Placebo is not considered in the calculation and removed from the dose grid for any calculations.

Usage

NextBestProbMTDLTE(target)

.DefaultNextBestProbMTDLTE()
NextBestProbMTDLTE(target)

.DefaultNextBestProbMTDLTE()

Arguments

target

(numeric)
see slot definition.

Slots

target: (numeric)
the target toxicity probability.

Note

Typically, end users will not use the .DefaultNextBestProbMTDLTE() function.

Examples

# In the example below, the MTD is defined as the dose with the highest
# probability of having a toxicity rate below 30%.
next_best_prob_mtd_lte <- NextBestProbMTDLTE(target = 0.3)
# In the example below, the MTD is defined as the dose with the highest
# probability of having a toxicity rate below 30%.
next_best_prob_mtd_lte <- NextBestProbMTDLTE(target = 0.3)

`NextBestProbMTDMinDist`

Description

NextBestProbMTDMinDist is the class of finding a next best dose that selects the dose with the highest probability of having a toxicity rate with the smallest distance to the toxicity target. The dose is determined by calculating the posterior toxicity probability for each dose per iteration and select the dose that has the smallest toxicity probability distance to the target. The dose with the highest frequency of being selected as MTD across iterations is the next best dose. Placebo is not considered as the next dose and for that reason not used in calculations. I.e. for placebo the toxicity probability distance to target is not calculated and taken into account for determination of the next dose.

Usage

NextBestProbMTDMinDist(target)

.DefaultNextBestProbMTDMinDist()
NextBestProbMTDMinDist(target)

.DefaultNextBestProbMTDMinDist()

Arguments

target

(numeric)
see slot definition.

Slots

target: (numeric)
the target toxicity probability.

Note

Typically, end users will not use the .DefaultNextBestProbMTDMinDist() function.

Examples

# In the example below, the MTD is defined as the dose with the highest
# probability of having a toxicity rate with minimal distance
# to the target of 30%.
next_best_prob_mtd_min_dist <- NextBestProbMTDMinDist(target = 0.3)
# In the example below, the MTD is defined as the dose with the highest
# probability of having a toxicity rate with minimal distance
# to the target of 30%.
next_best_prob_mtd_min_dist <- NextBestProbMTDMinDist(target = 0.3)

`NextBestTD`

Description

NextBestTD is the class to find a next best dose based on pseudo DLT model without samples. Namely, it is to find two next best doses, one for allocation during the trial and the second for final recommendation at the end of a trial without involving any samples, i.e. only DLT responses will be incorporated for the dose-allocation. This is based solely on the probabilities of the occurrence of a DLT obtained by using the modal estimates of the model parameters. There are two target probabilities of the occurrence of a DLT that must be specified: target probability to be used during the trial and target probability to be used at the end of the trial. It is suitable to use it only with the ModelTox model class.

Usage

.DefaultNextBestTD()

NextBestTD(prob_target_drt, prob_target_eot)
.DefaultNextBestTD()

NextBestTD(prob_target_drt, prob_target_eot)

Arguments

prob_target_drt

(proportion)
see slot definition.

prob_target_eot

(proportion)
see slot definition.

Slots

prob_target_drt: (proportion)
the target probability (except 0 or 1) of the occurrence of a DLT to be used during the trial.
prob_target_eot: (proportion)
the target probability (except 0 or 1) of the occurrence of a DLT to be used at the end of the trial.

Note

Typically, end users will not use the .DefaultNextBestTD() function.

Examples

my_next_best <- NextBestTD(0.35, 0.3)
my_next_best <- NextBestTD(0.35, 0.3)

`NextBestTDsamples`

Description

NextBestTDsamples is the class to find a next best dose based on Pseudo DLT model with samples. Namely, it is to find two next best doses, one for allocation during the trial and the second for final recommendation at the end of a trial. Hence, there are two target probabilities of the occurrence of a DLT that must be specified: target probability to be used during the trial and target probability to be used at the end of the trial.

Usage

NextBestTDsamples(prob_target_drt, prob_target_eot, derive)

.DefaultNextBestTDsamples()
NextBestTDsamples(prob_target_drt, prob_target_eot, derive)

.DefaultNextBestTDsamples()

Arguments

prob_target_drt

(proportion)
see slot definition in NextBestTD.

prob_target_eot

(proportion)
see slot definition in NextBestTD.

derive

(function)
see slot definition.

Slots

derive: (function)
derives, based on a vector of posterior dose samples, the target dose that has the probability of the occurrence of DLT equals to either the prob_target_drt or prob_target_eot. It must therefore accept one and only one argument, which is a numeric vector, and return a number.

Note

Typically, end users will not use the .DefaultNextBestTDsamples() function.

Examples

# Target probability of the occurrence of a DLT during the trial is set to 0.35.
# Target probability of the occurrence of a DLT at the end of the trial is set to 0.3.
# We want the use the 30% posterior quantile (the 30th percentile) of the TD35
# (the dose level with probability of the DLT equals 0.35) and TD30 samples.
my_next_best <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)
# Target probability of the occurrence of a DLT during the trial is set to 0.35.
# Target probability of the occurrence of a DLT at the end of the trial is set to 0.3.
# We want the use the 30% posterior quantile (the 30th percentile) of the TD35
# (the dose level with probability of the DLT equals 0.35) and TD30 samples.
my_next_best <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

`NextBestThreePlusThree`

Description

NextBestThreePlusThree is the class for next best dose that implements the classical 3+3 dose recommendation. No input is required, hence this class has no slots.

Usage

NextBestThreePlusThree()

.DefaultNextBestThreePlusThree()
NextBestThreePlusThree()

.DefaultNextBestThreePlusThree()

Note

Typically, end users will not use the .DefaultNextBestThreePlusThree() function.

Examples

# Next best dose class object using the classical 3+3 design.
my_next_best <- NextBestThreePlusThree()
# Next best dose class object using the classical 3+3 design.
my_next_best <- NextBestThreePlusThree()

Number of Doses in Grid

Description

A function that gets the number of doses in grid. User can choose whether the placebo dose (if any) should be counted or not.

Usage

ngrid(object, ignore_placebo = TRUE, ...)

## S4 method for signature 'Data'
ngrid(object, ignore_placebo = TRUE, ...)
ngrid(object, ignore_placebo = TRUE, ...)

## S4 method for signature 'Data'
ngrid(object, ignore_placebo = TRUE, ...)

Arguments

object

(Data)
object with dose grid.

ignore_placebo

(flag)
should placebo dose (if any) not be counted?

...

further arguments passed to class-specific methods.

Value

integer the number of doses in grid.

Examples

my_data <- Data(
  x = c(10, 50, 90, 100, 0.001, 20, 30, 30),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(1L, 2L, 3L, 4L, 5L, 5L, 6L, 6L),
  doseGrid = c(0.001, seq(from = 10, to = 100, by = 10)),
  placebo = TRUE
)
ngrid(my_data)
ngrid(my_data, ignore_placebo = FALSE)
my_data <- Data(
  x = c(10, 50, 90, 100, 0.001, 20, 30, 30),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(1L, 2L, 3L, 4L, 5L, 5L, 6L, 6L),
  doseGrid = c(0.001, seq(from = 10, to = 100, by = 10)),
  placebo = TRUE
)
ngrid(my_data)
ngrid(my_data, ignore_placebo = FALSE)

`OneParExpPrior`

Description

OneParExpPrior is the class for a standard CRM with an exponential prior on the power parameter for the skeleton prior probabilities. It is an implementation of a version of the one-parameter CRM (O'Quigley et al. 1990).

Usage

OneParExpPrior(skel_probs, dose_grid, lambda)

.DefaultOneParExpPrior()
OneParExpPrior(skel_probs, dose_grid, lambda)

.DefaultOneParExpPrior()

Arguments

skel_probs

see slot definition.

dose_grid

(numeric)
dose grid. It must be must be a sorted vector of the same length as skel_probs.

lambda

see slot definition.

Slots

skel_fun: (function)
function to calculate the prior DLT probabilities.
skel_fun_inv: (function)
inverse function of skel_fun.
skel_probs: (numeric)
skeleton prior probabilities. This is a vector of unique and sorted probability values between 0 and 1.
lambda: (number)
rate parameter of prior exponential distribution for theta.

Note

Typically, end users will not use the .DefaultOneparExpPrior() function.

Typically, end users will not use the .DefaultOneParLogNormalPrior() function.

References

O'Quigley J, Pepe M, Fisher L (1990). “Continual reassessment method: a practical design for phase 1 clinical trials in cancer.” Biometrics, 46(1). doi:10.2307/2531628.

Examples

my_model <- OneParExpPrior(
  skel_probs = c(0.1, 0.3, 0.5, 0.7, 0.9),
  dose_grid = 1:5,
  lambda = 2
)
my_model <- OneParExpPrior(
  skel_probs = c(0.1, 0.3, 0.5, 0.7, 0.9),
  dose_grid = 1:5,
  lambda = 2
)

`OneParLogNormalPrior`

Description

OneParLogNormalPrior is the class for a standard CRM with a normal prior on the log power parameter for the skeleton prior probabilities.

Usage

OneParLogNormalPrior(skel_probs, dose_grid, sigma2)

.DefaultOneParLogNormalPrior()
OneParLogNormalPrior(skel_probs, dose_grid, sigma2)

.DefaultOneParLogNormalPrior()

Arguments

skel_probs

(numeric)
skeleton prior probabilities. This is a vector of unique and sorted probability values between 0 and 1.

dose_grid

(numeric)
dose grid. It must be must be a sorted vector of the same length as skel_probs.

sigma2

(number)
prior variance of log power parameter alpha.

Value

an instance of the OneParLogNormalPrior class

Slots

skel_fun: (function)
function to calculate the prior DLT probabilities.
skel_fun_inv: (function)
inverse function of skel_fun.
skel_probs: (numeric)
skeleton prior probabilities. This is a vector of unique and sorted probability values between 0 and 1.
sigma2: (number)
prior variance of log power parameter alpha.

Examples

my_model <- OneParLogNormalPrior(
  skel_probs = seq(from = 0.1, to = 0.9, length = 5),
  dose_grid = 1:5,
  sigma2 = 2
)
my_model <- OneParLogNormalPrior(
  skel_probs = seq(from = 0.1, to = 0.9, length = 5),
  dose_grid = 1:5,
  sigma2 = 2
)

Open / recruit backfill patients into a cohort?

Description

Usage

openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningMinDose'
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningMinCohorts'
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningNone'
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningMinResponses'
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningList'
openCohort(opening, cohort, data, dose, summary_fun, ...)

## S4 method for signature 'OpeningAll'
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningAny'
openCohort(opening, cohort, data, dose, ...)
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningMinDose'
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningMinCohorts'
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningNone'
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningMinResponses'
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningList'
openCohort(opening, cohort, data, dose, summary_fun, ...)

## S4 method for signature 'OpeningAll'
openCohort(opening, cohort, data, dose, ...)

## S4 method for signature 'OpeningAny'
openCohort(opening, cohort, data, dose, ...)

Arguments

opening

(Opening)
opening rule to be applied.

cohort

(int)
backfill cohort index.

data

(Data)
current trial data.

dose

(numeric)
the recommended next best dose.

...

further arguments (not used).

summary_fun

(function)
to apply to the list of results (e.g., all or any). Only used for OpeningList and its subclasses.

Value

TRUE if this backfill cohort can be opened / recruited into, FALSE otherwise.

Functions

openCohort(OpeningMinDose): method for OpeningMinDose class.
openCohort(OpeningMinCohorts): method for OpeningMinCohorts class.
openCohort(OpeningNone): method for OpeningNone class, which never opens a cohort, i.e. the trial design will not use backfilling.
openCohort(OpeningMinResponses): method for OpeningMinResponses class.
openCohort(OpeningList): method for OpeningList class.
openCohort(OpeningAll): method for OpeningAll class. Returns TRUE if ALL opening criteria are satisfied.
openCohort(OpeningAny): method for OpeningAny class. Returns TRUE if ANY opening criteria are satisfied.

Examples

# Create a simple data object with some dose information
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(1, 2, 3, 4, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Define the opening rule: open backfill cohorts when dose is at least 5
my_opening <- OpeningMinDose(min_dose = 5)

# Check if the first backfill cohort can be opened when the
# cohort has a dose 6, i.e. larger than 5.
# Note that `dose` is not used in this rule.
can_open <- openCohort(my_opening, cohort = 5, data = data, dose = 7)
can_open

# Check if the first backfill cohort can be opened when the
# cohort has a dose 3, i.e. smaller than 5.
# (should return FALSE because dose < min_dose)
can_open_low <- openCohort(my_opening, cohort = 4, data = data, dose = 15)
can_open_low
# Create a data object with multiple cohorts
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Define the opening rule: open backfill cohorts after at least 3 cohorts
my_opening <- OpeningMinCohorts(min_cohorts = 3)

# Check if a backfill cohort can be opened
# Current number of cohorts is 6, so it should open (6 >= 3)
can_open <- openCohort(my_opening, cohort = 1, data = data, dose = 25)
can_open
# Create an OpeningNone object which never opens backfill cohorts
opening <- OpeningNone()

# Create sample trial data
data <- Data(
  x = c(20, 30, 40, 50),
  y = c(0, 0, 1, 0),
  cohort = c(1, 2, 3, 4),
  doseGrid = seq(10, 100, by = 10)
)

# Create dose recommendation
dose <- 60

# Check if backfill cohort should be opened for cohort 5
# OpeningNone always returns FALSE
should_open <- openCohort(opening, cohort = 5, data = data, dose = dose)
print(should_open) # FALSE
# Create an OpeningMinResponses object requiring 2 responses at the cohort dose
opening <- OpeningMinResponses(min_responses = 2, include_lower_doses = FALSE)

# Create sample trial data with responses at different dose levels
data <- Data(
  x = c(10, 10, 20, 20, 20, 30, 30, 30),
  y = c(0, 1, 0, 1, 0, 1, 1, 0),
  response = c(1, 1, NA, 0, 1, NA, 1, 0),
  cohort = c(1, 1, 2, 2, 2, 3, 3, 3),
  doseGrid = seq(10, 100, by = 10)
)

# Check if backfill cohort can be opened at dose 20
# At dose 20, there is 1 response
should_open <- openCohort(opening, cohort = 2, data = data, dose = 30)
print(should_open) # FALSE

# Check at dose 10 where there are 2 responses
should_open_2 <- openCohort(opening, cohort = 1, data = data, dose = 30)
print(should_open_2) # TRUE
# Create sample trial data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(1, 2, 3, 4, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Create opening criteria:
# - opening1: open if dose >= 10
# - opening2: open if at least 5 cohorts have been treated
opening1 <- OpeningMinDose(min_dose = 10)
opening2 <- OpeningMinCohorts(min_cohorts = 5)

# Combine with AND logic: both must be true
opening_all <- opening1 & opening2

# Test if backfill cohort 7 can be opened
# Cohort 6 dose is 10 (>= 10: TRUE), max cohort is 6 (>= 5: TRUE), so AND is TRUE
should_open <- openCohort(opening_all, cohort = 6, data = data, dose = 20)
print(should_open) # TRUE

# Test if backfill cohort at dose 6 can be opened
# Cohort 4 dose is 3 (>= 10: FALSE), so AND is FALSE
should_open_2 <- openCohort(opening_all, cohort = 4, data = data, dose = 20)
print(should_open_2) # FALSE
# Create sample trial data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(1, 2, 3, 4, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Create opening criteria:
# - opening1: open if dose >= 20
# - opening2: open if at least 5 cohorts have been treated
opening1 <- OpeningMinDose(min_dose = 20)
opening2 <- OpeningMinCohorts(min_cohorts = 5)

# Combine with OR logic: at least one must be true
opening_any <- opening1 | opening2

# Test if backfill cohort 7 can be opened at dose 10
# Cohort 6 dose is 10 (>= 20: FALSE), max cohort is 6 (>= 5: TRUE), so OR is TRUE
should_open <- openCohort(opening_any, cohort = 6, data = data, dose = 20)
print(should_open) # TRUE

# Test with a different scenario:
# It does not matter which cohort index we give, because
# the number of cohorts is already above 5.
should_open_2 <- openCohort(opening_any, cohort = 1, data = data, dose = 25)
print(should_open_2) # TRUE
# Create a simple data object with some dose information
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(1, 2, 3, 4, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Define the opening rule: open backfill cohorts when dose is at least 5
my_opening <- OpeningMinDose(min_dose = 5)

# Check if the first backfill cohort can be opened when the
# cohort has a dose 6, i.e. larger than 5.
# Note that `dose` is not used in this rule.
can_open <- openCohort(my_opening, cohort = 5, data = data, dose = 7)
can_open

# Check if the first backfill cohort can be opened when the
# cohort has a dose 3, i.e. smaller than 5.
# (should return FALSE because dose < min_dose)
can_open_low <- openCohort(my_opening, cohort = 4, data = data, dose = 15)
can_open_low
# Create a data object with multiple cohorts
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Define the opening rule: open backfill cohorts after at least 3 cohorts
my_opening <- OpeningMinCohorts(min_cohorts = 3)

# Check if a backfill cohort can be opened
# Current number of cohorts is 6, so it should open (6 >= 3)
can_open <- openCohort(my_opening, cohort = 1, data = data, dose = 25)
can_open
# Create an OpeningNone object which never opens backfill cohorts
opening <- OpeningNone()

# Create sample trial data
data <- Data(
  x = c(20, 30, 40, 50),
  y = c(0, 0, 1, 0),
  cohort = c(1, 2, 3, 4),
  doseGrid = seq(10, 100, by = 10)
)

# Create dose recommendation
dose <- 60

# Check if backfill cohort should be opened for cohort 5
# OpeningNone always returns FALSE
should_open <- openCohort(opening, cohort = 5, data = data, dose = dose)
print(should_open) # FALSE
# Create an OpeningMinResponses object requiring 2 responses at the cohort dose
opening <- OpeningMinResponses(min_responses = 2, include_lower_doses = FALSE)

# Create sample trial data with responses at different dose levels
data <- Data(
  x = c(10, 10, 20, 20, 20, 30, 30, 30),
  y = c(0, 1, 0, 1, 0, 1, 1, 0),
  response = c(1, 1, NA, 0, 1, NA, 1, 0),
  cohort = c(1, 1, 2, 2, 2, 3, 3, 3),
  doseGrid = seq(10, 100, by = 10)
)

# Check if backfill cohort can be opened at dose 20
# At dose 20, there is 1 response
should_open <- openCohort(opening, cohort = 2, data = data, dose = 30)
print(should_open) # FALSE

# Check at dose 10 where there are 2 responses
should_open_2 <- openCohort(opening, cohort = 1, data = data, dose = 30)
print(should_open_2) # TRUE
# Create sample trial data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(1, 2, 3, 4, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Create opening criteria:
# - opening1: open if dose >= 10
# - opening2: open if at least 5 cohorts have been treated
opening1 <- OpeningMinDose(min_dose = 10)
opening2 <- OpeningMinCohorts(min_cohorts = 5)

# Combine with AND logic: both must be true
opening_all <- opening1 & opening2

# Test if backfill cohort 7 can be opened
# Cohort 6 dose is 10 (>= 10: TRUE), max cohort is 6 (>= 5: TRUE), so AND is TRUE
should_open <- openCohort(opening_all, cohort = 6, data = data, dose = 20)
print(should_open) # TRUE

# Test if backfill cohort at dose 6 can be opened
# Cohort 4 dose is 3 (>= 10: FALSE), so AND is FALSE
should_open_2 <- openCohort(opening_all, cohort = 4, data = data, dose = 20)
print(should_open_2) # FALSE
# Create sample trial data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(1, 2, 3, 4, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Create opening criteria:
# - opening1: open if dose >= 20
# - opening2: open if at least 5 cohorts have been treated
opening1 <- OpeningMinDose(min_dose = 20)
opening2 <- OpeningMinCohorts(min_cohorts = 5)

# Combine with OR logic: at least one must be true
opening_any <- opening1 | opening2

# Test if backfill cohort 7 can be opened at dose 10
# Cohort 6 dose is 10 (>= 20: FALSE), max cohort is 6 (>= 5: TRUE), so OR is TRUE
should_open <- openCohort(opening_any, cohort = 6, data = data, dose = 20)
print(should_open) # TRUE

# Test with a different scenario:
# It does not matter which cohort index we give, because
# the number of cohorts is already above 5.
should_open_2 <- openCohort(opening_any, cohort = 1, data = data, dose = 25)
print(should_open_2) # TRUE

`Opening`

Description

Opening is a virtual class for opening criteria, from which all other specific opening criteria classes inherit. The subclasses are used for backfill cohort designs.

Usage

.DefaultOpening()
.DefaultOpening()

Note

Typically, end users will not use the .DefaultOpening() function.

`OpeningAll`

Description

OpeningAll combines multiple Opening objects using AND logic. A backfill cohort is opened only if ALL opening criteria in the list are satisfied. This can also be created using the & operator.

Usage

OpeningAll(...)

.DefaultOpeningAll()
OpeningAll(...)

.DefaultOpeningAll()

Arguments

...

(Opening) opening objects to combine with AND logic.

Slots

open_list: (list) a list of Opening objects to be combined with AND logic.

Note

Typically, end users will not use the .DefaultOpeningAll() function.

Examples

# Create two opening criteria
opening1 <- OpeningMinDose(min_dose = 10)
opening2 <- OpeningMinCohorts(min_cohorts = 3)

# Combine them with AND logic: both must be satisfied
opening_all <- OpeningAll(opening1, opening2)
print(opening_all)

# Alternative: use the & operator
opening_all_alt <- opening1 & opening2
print(opening_all_alt)
# Create two opening criteria
opening1 <- OpeningMinDose(min_dose = 10)
opening2 <- OpeningMinCohorts(min_cohorts = 3)

# Combine them with AND logic: both must be satisfied
opening_all <- OpeningAll(opening1, opening2)
print(opening_all)

# Alternative: use the & operator
opening_all_alt <- opening1 & opening2
print(opening_all_alt)

`OpeningAny`

Description

OpeningAny combines multiple Opening objects using OR logic. A backfill cohort is opened if ANY opening criteria in the list are satisfied. This can also be created using the | operator.

Usage

OpeningAny(...)

.DefaultOpeningAny()
OpeningAny(...)

.DefaultOpeningAny()

Arguments

...

(Opening) opening objects to combine with OR logic.

Slots

open_list: (list)
a list of Opening objects to be combined with OR logic.

Note

Typically, end users will not use the .DefaultOpeningAny() function.

Examples

# Create two opening criteria
opening1 <- OpeningMinDose(min_dose = 10)
opening2 <- OpeningMinCohorts(min_cohorts = 3)

# Combine them with OR logic: at least one must be satisfied
opening_any <- OpeningAny(opening1, opening2)
print(opening_any)

# Alternative: use the | operator
opening_any_alt <- opening1 | opening2
print(opening_any_alt)
# Create two opening criteria
opening1 <- OpeningMinDose(min_dose = 10)
opening2 <- OpeningMinCohorts(min_cohorts = 3)

# Combine them with OR logic: at least one must be satisfied
opening_any <- OpeningAny(opening1, opening2)
print(opening_any)

# Alternative: use the | operator
opening_any_alt <- opening1 | opening2
print(opening_any_alt)

`OpeningList`

Description

OpeningList is a virtual class for combining multiple Opening objects using logical operators. It is used as a base class for OpeningAll and OpeningAny.

Usage

OpeningList(...)

.DefaultOpeningList()
OpeningList(...)

.DefaultOpeningList()

Arguments

...

(Opening)
opening objects to combine.

Slots

open_list: (list) a list of Opening objects to be combined.

Note

Typically, end users will not use the .DefaultOpeningList() function.

Examples

# Create two simple opening criteria
opening1 <- OpeningMinDose(min_dose = 10)
opening2 <- OpeningMinCohorts(min_cohorts = 3)

# Create an OpeningList that combines them
opening_list <- OpeningList(opening1, opening2)
print(opening_list)

# You can also create with more than two criteria
opening3 <- OpeningNone()
opening_list_multi <- OpeningList(opening1, opening2, opening3)
print(opening_list_multi)
# Create two simple opening criteria
opening1 <- OpeningMinDose(min_dose = 10)
opening2 <- OpeningMinCohorts(min_cohorts = 3)

# Create an OpeningList that combines them
opening_list <- OpeningList(opening1, opening2)
print(opening_list)

# You can also create with more than two criteria
opening3 <- OpeningNone()
opening_list_multi <- OpeningList(opening1, opening2, opening3)
print(opening_list_multi)

`OpeningMinCohorts`

Description

OpeningMinCohorts opens backfill cohorts when the overall number of cohorts treated so far in the trial reaches or exceeds a minimum threshold. This can be used to implement a "delayed backfill cohort opening" rule.

Usage

OpeningMinCohorts(min_cohorts = 2L)

.DefaultOpeningMinCohorts()
OpeningMinCohorts(min_cohorts = 2L)

.DefaultOpeningMinCohorts()

Arguments

min_cohorts

(integer)
see slot definition.

Slots

min_cohorts: (integer)
the minimum number of cohorts that must have been treated before backfilling can be opened.

Note

Typically, end users will not use the .DefaultOpeningMinCohorts() function.

`OpeningMinDose`

Description

OpeningMinDose opens a backfill cohort when the cohort's dose is above or equal to the minimum dose specified. Note that the next recommended dose is not taken into account.

Usage

OpeningMinDose(min_dose = 0)

.DefaultOpeningMinDose()
OpeningMinDose(min_dose = 0)

.DefaultOpeningMinDose()

Arguments

min_dose

(number)
see slot definition.

Slots

min_dose: (number)
the minimum dose at which backfill cohorts can be opened.

Note

Typically, end users will not use the .DefaultOpeningMinDose() function.

Examples

# Opening backfill cohort when dose is at least 50
my_opening <- OpeningMinDose(min_dose = 50)
# Opening backfill cohort when dose is at least 50
my_opening <- OpeningMinDose(min_dose = 50)

`OpeningMinResponses`

Description

OpeningMinResponses opens backfill cohorts when a minimum number of responses has been observed in the trial. The responses can be counted at the cohort's dose level only, or also at lower dose levels if include_lower_doses is set to TRUE.

Usage

OpeningMinResponses(min_responses = 1L, include_lower_doses = FALSE)

.DefaultOpeningMinResponses()
OpeningMinResponses(min_responses = 1L, include_lower_doses = FALSE)

.DefaultOpeningMinResponses()

Arguments

min_responses

(count)
see slot definition.

include_lower_doses

(logical)
see slot definition.

Slots

min_responses: (count)
the minimum number of responses required before backfill cohorts can be opened (at least 1).
include_lower_doses: (logical)
if TRUE, responses at all doses less than or equal to the cohort's dose are counted. If FALSE, only responses at the cohort's dose are counted.

Note

Typically, end users will not use the .DefaultOpeningMinResponses() function.

Examples

# Create an OpeningMinResponses object that requires 2 responses
opening <- OpeningMinResponses(min_responses = 2, include_lower_doses = FALSE)

# Display the object
print(opening)

# Create a variant that includes lower doses
opening_inclusive <- OpeningMinResponses(
  min_responses = 2,
  include_lower_doses = TRUE
)
print(opening_inclusive)
# Create an OpeningMinResponses object that requires 2 responses
opening <- OpeningMinResponses(min_responses = 2, include_lower_doses = FALSE)

# Display the object
print(opening)

# Create a variant that includes lower doses
opening_inclusive <- OpeningMinResponses(
  min_responses = 2,
  include_lower_doses = TRUE
)
print(opening_inclusive)

`OpeningNone`

Description

OpeningNone never opens any backfill cohorts. This can be used when no backfill cohorts should be available in a trial design.

Usage

OpeningNone()

.DefaultOpeningNone()
OpeningNone()

.DefaultOpeningNone()

Note

Typically, end users will not use the .DefaultOpeningNone() function.

Combine Two Stopping Rules with OR

Description

The method combining two atomic stopping rules.

Usage

## S4 method for signature 'Stopping,Stopping'
e1 | e2
## S4 method for signature 'Stopping,Stopping'
e1 | e2

Arguments

e1

(Stopping)
first stopping rule object.

e2

(Stopping)
second stopping rule object.

Value

The StoppingAny object.

Examples

## Example of combining two atomic stopping rules with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping <- myStopping1 | myStopping2
## Example of combining two atomic stopping rules with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping <- myStopping1 | myStopping2

Combine an Atomic Stopping Rule and a Stopping List with OR

Description

The method combining an atomic stopping rule and a stopping list.

Usage

## S4 method for signature 'Stopping,StoppingAny'
e1 | e2
## S4 method for signature 'Stopping,StoppingAny'
e1 | e2

Arguments

e1

(Stopping)
stopping rule object.

e2

(StoppingAny)
stopping list object.

Value

The modified StoppingAny object.

Examples

## Example of combining an atomic stopping rule with a list of stopping rules
## with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping3 <- StoppingMinPatients(nPatients = 20)

myStopping <- myStopping3 | (myStopping1 & myStopping2)
## Example of combining an atomic stopping rule with a list of stopping rules
## with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping3 <- StoppingMinPatients(nPatients = 20)

myStopping <- myStopping3 | (myStopping1 & myStopping2)

Combine a Stopping List and an Atomic Stopping Rule with OR

Description

The method combining a stopping list and an atomic stopping rule.

Usage

## S4 method for signature 'StoppingAny,Stopping'
e1 | e2
## S4 method for signature 'StoppingAny,Stopping'
e1 | e2

Arguments

e1

(StoppingAny)
stopping list object.

e2

(Stopping)
stopping rule object.

Value

The modified StoppingAny object.

Examples

## Example of combining a list of stopping rules with an atomic stopping rule
## with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping3 <- StoppingMinPatients(nPatients = 20)

myStopping <- (myStopping1 & myStopping2) | myStopping3
## Example of combining a list of stopping rules with an atomic stopping rule
## with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

myStopping3 <- StoppingMinPatients(nPatients = 20)

myStopping <- (myStopping1 & myStopping2) | myStopping3

Logical OR Operator for Opening Objects

Description

Combines two Opening objects with OR logic using the | operator. This creates an OpeningAny object.

Usage

## S4 method for signature 'Opening,Opening'
e1 | e2
## S4 method for signature 'Opening,Opening'
e1 | e2

Arguments

e1

(Opening) the first opening object.

e2

(Opening) the second opening object.

Value

An OpeningAny object combining e1 and e2.

Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples

Description

Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples

Usage

## S4 method for signature 'Data,ModelTox'
plot(
  x,
  y,
  xlab = "Dose level",
  ylab = "Probability of DLE",
  showLegend = TRUE,
  ...
)
## S4 method for signature 'Data,ModelTox'
plot(
  x,
  y,
  xlab = "Dose level",
  ylab = "Probability of DLE",
  showLegend = TRUE,
  ...
)

Arguments

x

the data of Data class object

y

the model of the ModelTox class object

xlab

the x axis label

ylab

the y axis label

showLegend

should the legend be shown? (default)

...

not used

Value

This returns the ggplot object for the dose-DLE model plot

Examples

## plot the dose-DLE curve given a pseudo DLE model using data without samples
## data must be of 'Data' class
## define the data
data <- Data(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  ID = 1L:8L,
  cohort = as.integer(c(1, 2, 2, 3, 4, 4, 5, 6)),
  doseGrid = seq(25, 300, 25)
)
## model must be from 'ModelTox' class e.g 'LogisticIndepBeta' class model
## define the model (see LogisticIndepBeta example)
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## plot the dose-DLE curve
## 'x' is the data and 'y' is the model in plot
plot(x = data, y = model)
## plot the dose-DLE curve given a pseudo DLE model using data without samples
## data must be of 'Data' class
## define the data
data <- Data(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  ID = 1L:8L,
  cohort = as.integer(c(1, 2, 2, 3, 4, 4, 5, 6)),
  doseGrid = seq(25, 300, 25)
)
## model must be from 'ModelTox' class e.g 'LogisticIndepBeta' class model
## define the model (see LogisticIndepBeta example)
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## plot the dose-DLE curve
## 'x' is the data and 'y' is the model in plot
plot(x = data, y = model)

Plot Method for the `DataDA` Class

Description

A method that creates a plot for DataDA object.

Usage

## S4 method for signature 'DataDA,missing'
plot(x, y, blind = FALSE, ...)
## S4 method for signature 'DataDA,missing'
plot(x, y, blind = FALSE, ...)

Arguments

x

(DataDA)
object we want to plot.

y

(missing)
missing object, for compatibility with the generic function.

...

passed to the first inherited method plot after this current method.

Value

The ggplot2::ggplot2 object.

Examples

# Create some data of class 'DataDA'.
my_data <- DataDA(
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60,
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Plot the data.
plot(my_data)
# Create some data of class 'DataDA'.
my_data <- DataDA(
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60,
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Plot the data.
plot(my_data)

Plot Method for the `DataDual` Class

Description

A method that creates a plot for DataDual object.

Usage

## S4 method for signature 'DataDual,missing'
plot(x, y, blind = FALSE, ...)
## S4 method for signature 'DataDual,missing'
plot(x, y, blind = FALSE, ...)

Arguments

x

(DataDual)
object we want to plot.

y

(missing)
missing object, for compatibility with the generic function.

...

passed to the first inherited method plot after this current method.

Value

The ggplot2::ggplot2 object.

Examples

# Create some data of class 'DataDual'.
my_data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  w = rnorm(8),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Plot the data.
plot(my_data)
# Create some data of class 'DataDual'.
my_data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  w = rnorm(8),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Plot the data.
plot(my_data)

Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples

Description

Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples

Usage

## S4 method for signature 'DataDual,ModelEff'
plot(
  x,
  y,
  ...,
  xlab = "Dose level",
  ylab = "Expected Efficacy",
  showLegend = TRUE
)
## S4 method for signature 'DataDual,ModelEff'
plot(
  x,
  y,
  ...,
  xlab = "Dose level",
  ylab = "Expected Efficacy",
  showLegend = TRUE
)

Arguments

x

the data of DataDual class object

y

the model of the ModelEff class object

...

not used

xlab

the x axis label

ylab

the y axis label

showLegend

should the legend be shown? (default)

Value

This returns the ggplot object for the dose-efficacy model plot

Examples

# nolint start

##plot the dose-efficacy curve given a pseudo efficacy model using data without samples
##data must be of 'DataDual' class
##define the data
data <- DataDual(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##model must be from 'ModelEff' class e.g 'Effloglog' class model
##define the model (see Effloglog example)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
## plot the dose-efficacy curve
## 'x' is the data and 'y' is the model in plot
plot(x = data, y = Effmodel)

# nolint end
# nolint start

##plot the dose-efficacy curve given a pseudo efficacy model using data without samples
##data must be of 'DataDual' class
##define the data
data <- DataDual(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##model must be from 'ModelEff' class e.g 'Effloglog' class model
##define the model (see Effloglog example)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
## plot the dose-efficacy curve
## 'x' is the data and 'y' is the model in plot
plot(x = data, y = Effmodel)

# nolint end

Plot `DualSimulations`

Description

This plot method can be applied to DualSimulations objects in order to summarize them graphically. In addition to the standard plot types, there is:

sigma2W: Plot a boxplot of the final biomarker variance estimates in the simulated trials
rho: Plot a boxplot of the final correlation estimates in the simulated trials

Usage

## S4 method for signature 'DualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2W", "rho"), ...)
## S4 method for signature 'DualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2W", "rho"), ...)

Arguments

x

(DualSimulations)
the object we want to plot from.

y

(missing)
not used.

type

(character)
the type of plots you want to obtain.

...

not used.

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples

# Define the dose-grid.
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Create some data.
my_data <- DataDual(
  x = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    10,
    10,
    10,
    20,
    20,
    20,
    40,
    40,
    40,
    50,
    50,
    50
  ),
  y = c(
    0,
    0,
    0,
    0,
    0,
    0,
    1,
    0,
    0,
    1,
    1,
    0,
    0,
    1,
    0,
    1,
    1
  ),
  ID = 1:17,
  cohort = c(
    1L,
    2L,
    3L,
    4L,
    5L,
    6L,
    6L,
    6L,
    7L,
    7L,
    7L,
    8L,
    8L,
    8L,
    9L,
    9L,
    9L
  ),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
myStopping <- myStopping4 | StoppingMinPatients(40) | StoppingMissingDose()

my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping
my_stopping4 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
my_stopping <- my_stopping4 | StoppingMinPatients(40) | StoppingMissingDose()

# Choose the rule for dose increments
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design

my_design <- DualDesign(
  model = my_model,
  data = emptydata,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = CohortSizeConst(3),
  startingDose = 3
)

# Define scenarios for the TRUE toxicity and efficacy profiles.
beta_mod <- function(dose, e0, eMax, delta1, delta2, scal) {
  maxDens <- (delta1^delta1) *
    (delta2^delta2) /
    ((delta1 + delta2)^(delta1 + delta2))
  dose <- dose / scal
  e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
}

true_biomarker <- function(dose) {
  beta_mod(
    dose,
    e0 = 0.2,
    eMax = 0.6,
    delta1 = 5,
    delta2 = 5 * 0.5 / 0.5,
    scal = 100
  )
}

true_tox <- function(dose) {
  pnorm((dose - 60) / 10)
}

# Draw the TRUE profiles
par(mfrow = c(1, 2))
curve(true_tox(x), from = 0, to = 80)
curve(true_biomarker(x), from = 0, to = 80)

# Run the simulation on the desired design.
# We only generate 1 trial outcome here for illustration, for the actual study.
# Also for illustration purpose, we will use 5 burn-ins to generate 20 samples,
# this should be increased of course.
my_sims <- simulate(
  object = my_design,
  trueTox = true_tox,
  trueBiomarker = true_biomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 9,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 1,
    step = 1,
    samples = 2
  )
)

# Plot the results of the simulation.
print(plot(my_sims))
# Define the dose-grid.
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Create some data.
my_data <- DataDual(
  x = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    10,
    10,
    10,
    20,
    20,
    20,
    40,
    40,
    40,
    50,
    50,
    50
  ),
  y = c(
    0,
    0,
    0,
    0,
    0,
    0,
    1,
    0,
    0,
    1,
    1,
    0,
    0,
    1,
    0,
    1,
    1
  ),
  ID = 1:17,
  cohort = c(
    1L,
    2L,
    3L,
    4L,
    5L,
    6L,
    6L,
    6L,
    7L,
    7L,
    7L,
    8L,
    8L,
    8L,
    9L,
    9L,
    9L
  ),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
myStopping <- myStopping4 | StoppingMinPatients(40) | StoppingMissingDose()

my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping
my_stopping4 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
my_stopping <- my_stopping4 | StoppingMinPatients(40) | StoppingMissingDose()

# Choose the rule for dose increments
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design

my_design <- DualDesign(
  model = my_model,
  data = emptydata,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = CohortSizeConst(3),
  startingDose = 3
)

# Define scenarios for the TRUE toxicity and efficacy profiles.
beta_mod <- function(dose, e0, eMax, delta1, delta2, scal) {
  maxDens <- (delta1^delta1) *
    (delta2^delta2) /
    ((delta1 + delta2)^(delta1 + delta2))
  dose <- dose / scal
  e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
}

true_biomarker <- function(dose) {
  beta_mod(
    dose,
    e0 = 0.2,
    eMax = 0.6,
    delta1 = 5,
    delta2 = 5 * 0.5 / 0.5,
    scal = 100
  )
}

true_tox <- function(dose) {
  pnorm((dose - 60) / 10)
}

# Draw the TRUE profiles
par(mfrow = c(1, 2))
curve(true_tox(x), from = 0, to = 80)
curve(true_biomarker(x), from = 0, to = 80)

# Run the simulation on the desired design.
# We only generate 1 trial outcome here for illustration, for the actual study.
# Also for illustration purpose, we will use 5 burn-ins to generate 20 samples,
# this should be increased of course.
my_sims <- simulate(
  object = my_design,
  trueTox = true_tox,
  trueBiomarker = true_biomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 9,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 1,
    step = 1,
    samples = 2
  )
)

# Plot the results of the simulation.
print(plot(my_sims))

Plot Dual-Endpoint Design Simulation Summary

Description

Graphical display of dual-endpoint simulation summary.

This plot method can be applied to DualSimulationsSummary objects in order to summarize them graphically. Possible type of plots at the moment are those listed in plot,SimulationsSummary,missing-method plus:

meanBiomarkerFit: Plot showing the average fitted dose-biomarker curve across the trials, together with 95% credible intervals, and comparison with the assumed truth (as specified by the trueBiomarker argument to summary,DualSimulations-method)

You can specify any subset of these in the type argument.

Usage

## S4 method for signature 'DualSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit",
    "meanBiomarkerFit"),
  ...
)
## S4 method for signature 'DualSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit",
    "meanBiomarkerFit"),
  ...
)

Arguments

x

(DualSimulationsSummary)
the object we want to plot from.

y

(missing)
not used.

type

(character)
the types of plots you want to obtain.

...

not used.

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples

# Define the dose-grid.
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))


# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)


# Choose the rule for selecting the next dose.
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping4 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
# Only 10 patients here for illustration!
my_stopping <- my_stopping4 | StoppingMinPatients(10) | StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
my_design <- DualDesign(
  model = my_model,
  data = emptydata,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = CohortSizeConst(3),
  startingDose = 3
)

# Define scenarios for the TRUE toxicity and efficacy profiles.
beta_mod <- function(dose, e0, eMax, delta1, delta2, scal) {
  maxDens <- (delta1^delta1) *
    (delta2^delta2) /
    ((delta1 + delta2)^(delta1 + delta2))
  dose <- dose / scal
  e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
}


true_biomarker <- function(dose) {
  beta_mod(
    dose,
    e0 = 0.2,
    eMax = 0.6,
    delta1 = 5,
    delta2 = 5 * 0.5 / 0.5,
    scal = 100
  )
}

true_tox <- function(dose) {
  pnorm((dose - 60) / 10)
}

# Draw the TRUE profiles.
par(mfrow = c(1, 2))
curve(true_tox(x), from = 0, to = 80)
curve(true_biomarker(x), from = 0, to = 80)

# Run the simulation on the desired design.
# We only generate 1 trial outcome here for illustration, for the actual study.
# For illustration purpose we will use 5 burn-ins to generate 20 samples,
# this should be increased of course.
my_sims <- simulate(
  object = my_design,
  trueTox = true_tox,
  trueBiomarker = true_biomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 3,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 5,
    step = 1,
    samples = 20
  )
)

# Plot the summary of the Simulations.
plot(summary(my_sims, trueTox = true_tox, trueBiomarker = true_biomarker))
# Define the dose-grid.
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))


# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)


# Choose the rule for selecting the next dose.
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping4 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
# Only 10 patients here for illustration!
my_stopping <- my_stopping4 | StoppingMinPatients(10) | StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
my_design <- DualDesign(
  model = my_model,
  data = emptydata,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = CohortSizeConst(3),
  startingDose = 3
)

# Define scenarios for the TRUE toxicity and efficacy profiles.
beta_mod <- function(dose, e0, eMax, delta1, delta2, scal) {
  maxDens <- (delta1^delta1) *
    (delta2^delta2) /
    ((delta1 + delta2)^(delta1 + delta2))
  dose <- dose / scal
  e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
}


true_biomarker <- function(dose) {
  beta_mod(
    dose,
    e0 = 0.2,
    eMax = 0.6,
    delta1 = 5,
    delta2 = 5 * 0.5 / 0.5,
    scal = 100
  )
}

true_tox <- function(dose) {
  pnorm((dose - 60) / 10)
}

# Draw the TRUE profiles.
par(mfrow = c(1, 2))
curve(true_tox(x), from = 0, to = 80)
curve(true_biomarker(x), from = 0, to = 80)

# Run the simulation on the desired design.
# We only generate 1 trial outcome here for illustration, for the actual study.
# For illustration purpose we will use 5 burn-ins to generate 20 samples,
# this should be increased of course.
my_sims <- simulate(
  object = my_design,
  trueTox = true_tox,
  trueBiomarker = true_biomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 3,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 5,
    step = 1,
    samples = 20
  )
)

# Plot the summary of the Simulations.
plot(summary(my_sims, trueTox = true_tox, trueBiomarker = true_biomarker))

Plot `GeneralSimulations`

Description

Summarize the simulations with plots.

This plot method can be applied to GeneralSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory: Summary of the trajectory of the simulated trials
dosesTried: Average proportions of the doses tested in patients

You can specify one or both of these in the type argument.

Usage

## S4 method for signature 'GeneralSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried"), ...)
## S4 method for signature 'GeneralSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried"), ...)

Arguments

x

(GeneralSimulations)
the object we want to plot from.

y

(missing)
not used.

type

(character)
the type of plots you want to obtain.

...

not used.

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples

# nolint start

## obtain the plot for the simulation results
## If only DLE responses are considered in the simulations

## Specified your simulations when no DLE samples are used
## Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid = seq(25, 300, 25))

## Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## Then the escalation rule
tdNextBest <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

## The cohort size, size of 3 subjects
mySize <- CohortSizeConst(size = 3)
## Deifne the increments for the dose-escalation process
## The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
## The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
## This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements <- IncrementsRelative(
  intervals = c(min(data@doseGrid), max(data@doseGrid)),
  increments = c(2, 2)
)
## Specified the stopping rule e.g stop when the maximum sample size of 12 patients has been reached
myStopping <- StoppingMinPatients(nPatients = 12) | StoppingMissingDose()
## Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)

## Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

## Then specified the simulations and generate the trial
## For illustration purpose only 1 simulation is produced (nsim=1).
## The simulations
mySim <- simulate(
  design,
  args = NULL,
  truth = myTruth,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)


## plot the simulations
print(plot(mySim))


## If DLE samples are involved
## The escalation rule
tdNextBest <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)
## specify the design
design <- TDsamplesDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)
## options for MCMC
## The simulations
## For illustration purpose only 1 simulation is produced (nsim=1).
# mySim <- simulate(design,
#                   args=NULL,
#                   truth=myTruth,
#                   nsim=1,
#                   seed=819,
#                   mcmcOptions=options,
#                   parallel=FALSE)
#
# ##plot the simulations
# print(plot(mySim))
#

# nolint end
# nolint start

## obtain the plot for the simulation results
## If only DLE responses are considered in the simulations

## Specified your simulations when no DLE samples are used
## Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid = seq(25, 300, 25))

## Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## Then the escalation rule
tdNextBest <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

## The cohort size, size of 3 subjects
mySize <- CohortSizeConst(size = 3)
## Deifne the increments for the dose-escalation process
## The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
## The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
## This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements <- IncrementsRelative(
  intervals = c(min(data@doseGrid), max(data@doseGrid)),
  increments = c(2, 2)
)
## Specified the stopping rule e.g stop when the maximum sample size of 12 patients has been reached
myStopping <- StoppingMinPatients(nPatients = 12) | StoppingMissingDose()
## Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)

## Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

## Then specified the simulations and generate the trial
## For illustration purpose only 1 simulation is produced (nsim=1).
## The simulations
mySim <- simulate(
  design,
  args = NULL,
  truth = myTruth,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)


## plot the simulations
print(plot(mySim))


## If DLE samples are involved
## The escalation rule
tdNextBest <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)
## specify the design
design <- TDsamplesDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)
## options for MCMC
## The simulations
## For illustration purpose only 1 simulation is produced (nsim=1).
# mySim <- simulate(design,
#                   args=NULL,
#                   truth=myTruth,
#                   nsim=1,
#                   seed=819,
#                   mcmcOptions=options,
#                   parallel=FALSE)
#
# ##plot the simulations
# print(plot(mySim))
#

# nolint end

Plot `GeneralSimulationsSummary`

Description

Graphical display of the general simulation summary.

This plot method can be applied to GeneralSimulationsSummary objects in order to summarize them graphically. Possible types of plots at the moment are:

nObs: Distribution of the number of patients in the simulated trials
doseSelected: Distribution of the final selected doses in the trials. Note that this can include zero entries, meaning that the trial was stopped because all doses in the dose grid appeared too toxic.
propDLTs: Distribution of the proportion of patients with DLTs in the trials
nAboveTarget: Distribution of the number of patients treated at doses which are above the target toxicity interval (as specified by the truth and target arguments to summary,GeneralSimulations-method)

You can specify any subset of these in the type argument.

Usage

## S4 method for signature 'GeneralSimulationsSummary,missing'
plot(x, y, type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget"), ...)
## S4 method for signature 'GeneralSimulationsSummary,missing'
plot(x, y, type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget"), ...)

Arguments

x

(GeneralSimulationsSummary)
the object we want to plot from.

y

(missing)
not used.

type

(character)
the types of plots you want to obtain.

...

not used.

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Plot `PseudoDualFlexiSimulations`

Description

Summarize the simulations with plots.

This plot method can be applied to PseudoDualFlexiSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory: Summary of the trajectory of the simulated trials
dosesTried: Average proportions of the doses tested in patients
sigma2: The variance of the efficacy responses
sigma2betaW: The variance of the random walk model

You can specify one or both of these in the type argument.

Usage

## S4 method for signature 'PseudoDualFlexiSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2", "sigma2betaW"), ...)
## S4 method for signature 'PseudoDualFlexiSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2", "sigma2betaW"), ...)

Arguments

x

(PseudoDualFlexiSimulations)
the object we want to plot from.

y

(missing)
missing object, not used.

type

(character)
the type of plots you want to obtain.

...

not used.

Value

A single ggplot2 object if a single plot is asked for, otherwise a gtable object.

Examples

# Obtain the plot for the simulation results if DLE and efficacy responses
# are considered in the simulations.
emptydata <- DataDual(doseGrid = seq(25, 300, 25))

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model must be of 'EffFlexi' class.
eff_model <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGainSamples' class.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# The cohort size, size of 3 subjects.
my_size <- CohortSizeConst(size = 3)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Define the stopping rule. Stop when the maximum sample size of 36 patients has
# been reached or when the next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design.
design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)
# Specify the true DLE curve and the true expected efficacy values
# at all dose levels.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)

my_truth_eff <- c(
  -0.5478867,
  0.1645417,
  0.5248031,
  0.7604467,
  0.9333009,
  1.0687031,
  1.1793942,
  1.2726408,
  1.3529598,
  1.4233411,
  1.4858613,
  1.5420182
)

# The true gain curve.
my_truth_gain <- function(dose) {
  return((myTruthEff(dose)) / (1 + (myTruthDLE(dose) / (1 - myTruthDLE(dose)))))
}

# MCMC options.
my_options <- McmcOptions(burnin = 10, step = 1, samples = 20)

# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueSigma2 = 0.025,
  trueSigma2betaW = 1,
  mcmcOptions = my_options,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# Plot the simulated results.
print(plot(my_sim))
# Obtain the plot for the simulation results if DLE and efficacy responses
# are considered in the simulations.
emptydata <- DataDual(doseGrid = seq(25, 300, 25))

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model must be of 'EffFlexi' class.
eff_model <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGainSamples' class.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# The cohort size, size of 3 subjects.
my_size <- CohortSizeConst(size = 3)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Define the stopping rule. Stop when the maximum sample size of 36 patients has
# been reached or when the next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design.
design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)
# Specify the true DLE curve and the true expected efficacy values
# at all dose levels.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)

my_truth_eff <- c(
  -0.5478867,
  0.1645417,
  0.5248031,
  0.7604467,
  0.9333009,
  1.0687031,
  1.1793942,
  1.2726408,
  1.3529598,
  1.4233411,
  1.4858613,
  1.5420182
)

# The true gain curve.
my_truth_gain <- function(dose) {
  return((myTruthEff(dose)) / (1 + (myTruthDLE(dose) / (1 - myTruthDLE(dose)))))
}

# MCMC options.
my_options <- McmcOptions(burnin = 10, step = 1, samples = 20)

# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueSigma2 = 0.025,
  trueSigma2betaW = 1,
  mcmcOptions = my_options,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# Plot the simulated results.
print(plot(my_sim))

Plot `PseudoDualSimulations`

Description

Summarize the simulations with plots.

This plot method can be applied to PseudoDualSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory: Summary of the trajectory of the simulated trials
dosesTried: Average proportions of the doses tested in patients
sigma2: The variance of the efficacy responses

You can specify one or both of these in the type argument.

Usage

## S4 method for signature 'PseudoDualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2"), ...)
## S4 method for signature 'PseudoDualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2"), ...)

Arguments

x

(PseudoDualSimulations)
the object we want to plot from.

y

(missing)
missing object, not used.

type

(character)
the type of plots you want to obtain.

...

not used.

Value

A single ggplot2 object if a single plot is asked for, otherwise a gtable object.

Examples

# Obtain the plot for the simulation results if DLE and efficacy responses
# are considered in the simulations.

# Example to run simulations when no samples are used. The data object
# must be defined with doses >= 1:
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model must be of 'ModelEff' (e.g 'Effloglog') class.
eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGain' class.
my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Cohort size of 3.
my_size <- CohortSizeConst(size = 3)

# Stop only when 36 subjects are treated or next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Now specify the design with all the above information and starting with a
# dose of 25 (for details please refer to the 'DualResponsesDesign' example).
my_design <- DualResponsesDesign(
  nextBest = my_next_best,
  model = dle_model,
  eff_model = eff_model,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the true DLE and efficacy curves.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)
my_truth_eff <- efficacyFunction(
  eff_model,
  theta1 = -4.818429,
  theta2 = 3.653058
)

# Run simulations (for illustration purpose only 1 simulation is produced).

my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# Plot the simulation results.
print(plot(my_sim))

# Example if DLE and efficacy samples are involved.
# The escalation rule using the 'NextBestMaxGainSamples' class.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# The design of 'DualResponsesSamplesDesign' class.
my_design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)

# Options for MCMC.
my_options <- McmcOptions(burnin = 10, step = 1, samples = 20)

# For illustration purpose only 1 simulation is produced (nsim = 1).
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 1,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)

# Plot the simulation results.
print(plot(my_sim))

# Obtain the plot for the simulation results if DLE and efficacy responses
# are considered in the simulations.

# Example to run simulations when no samples are used. The data object
# must be defined with doses >= 1:
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model must be of 'ModelEff' (e.g 'Effloglog') class.
eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGain' class.
my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Cohort size of 3.
my_size <- CohortSizeConst(size = 3)

# Stop only when 36 subjects are treated or next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Now specify the design with all the above information and starting with a
# dose of 25 (for details please refer to the 'DualResponsesDesign' example).
my_design <- DualResponsesDesign(
  nextBest = my_next_best,
  model = dle_model,
  eff_model = eff_model,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the true DLE and efficacy curves.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)
my_truth_eff <- efficacyFunction(
  eff_model,
  theta1 = -4.818429,
  theta2 = 3.653058
)

# Run simulations (for illustration purpose only 1 simulation is produced).

my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# Plot the simulation results.
print(plot(my_sim))

# Example if DLE and efficacy samples are involved.
# The escalation rule using the 'NextBestMaxGainSamples' class.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# The design of 'DualResponsesSamplesDesign' class.
my_design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)

# Options for MCMC.
my_options <- McmcOptions(burnin = 10, step = 1, samples = 20)

# For illustration purpose only 1 simulation is produced (nsim = 1).
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 1,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)

# Plot the simulation results.
print(plot(my_sim))

Plot `PseudoDualSimulationsSummary`

Description

Plot the summary of PseudoDualSimulations.

This plot method can be applied to PseudoDualSimulationsSummary objects in order to summarize them graphically. Possible type of plots at the moment are those listed in plot,PseudoSimulationsSummary,missing-method plus:

meanEffFit: Plot showing the fitted dose-efficacy curve. If no samples are involved, only the average fitted dose-efficacy curve across the trials will be plotted. If samples (DLE and efficacy) are involved, the average fitted dose-efficacy curve across the trials, together with the 95% credibility interval; and comparison with the assumed truth (as specified by the trueEff argument to summary,PseudoDualSimulations-method)

You can specify any subset of these in the type argument.

Usage

## S4 method for signature 'PseudoDualSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit",
    "meanEffFit"),
  ...
)
## S4 method for signature 'PseudoDualSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit",
    "meanEffFit"),
  ...
)

Arguments

x

(PseudoDualSimulationsSummary)
the object we want to plot from.

y

(missing)
not used.

type

(character)
the types of plots you want to obtain.

...

not used.

Value

A single ggplot2 object if a single plot is asked for, otherwise a gtable object.

Examples

# Obtain the summary plot for the simulation results if DLE and efficacy
# responses are considered in the simulations.

# In the example when no samples are used a data object with doses >= 1
# needs to be defined.
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model of 'ModelEff' (e.g 'Effloglog') class.
eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGain' class.
my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Cohort size of 3.
my_size <- CohortSizeConst(size = 3)

# Stop when 10 subjects are treated (for illustration only).
my_stopping <- StoppingMinPatients(nPatients = 10) | StoppingMissingDose()

## Now specified the design with all the above information and starting with a dose of 25

# Specify the design. (For details please refer to the 'DualResponsesDesign' example.)
my_design <- DualResponsesDesign(
  nextBest = my_next_best,
  model = dle_model,
  eff_model = eff_model,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the true DLE and efficacy curves.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)
my_truth_eff <- efficacyFunction(eff_model, theta1 = -4.818429, theta2 = 3.653058)


# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 1,
  mcmcOptions = McmcOptions(burnin = 10, step = 1, samples = 50),
  seed = 819,
  parallel = FALSE
)

# Summary of the simulations.
my_sum <- summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Plot the summary of the simulations.
print(plot(my_sum))


# Example where DLE and efficacy samples are involved.
# Please refer to design-method 'simulate DualResponsesSamplesDesign' examples
# for details.
# Specify the next best method.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# Specify the design.
my_design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)

# MCMC options.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)


# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 1,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)


# Generate a summary of the simulations.
my_sum <- summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Plot the summary of the simulations.
print(plot(my_sum))


# Example where the 'EffFlexi' class is used for the efficacy model.
eff_model <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = emptydata
)

# Specify the design.
my_design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)

# Specify the true DLE curve and the true expected efficacy values at all dose levels.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)

my_truth_eff <- c(
  -0.5478867, 0.1645417, 0.5248031, 0.7604467,
  0.9333009, 1.0687031, 1.1793942, 1.2726408,
  1.3529598, 1.4233411, 1.4858613, 1.5420182
)

# Define the true gain curve.
my_truth_gain <- function(dose) {
  return((my_truth_eff(dose)) / (1 + (my_truth_dle(dose) / (1 - my_truth_dle(dose)))))
}


## The simulations
## For illustration purpose only 1 simulation is produced (nsim=1).
mySim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueSigma2 = 0.025,
  trueSigma2betaW = 1,
  nsim = 1,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)

# Produce a summary of the simulations.
my_sum <- summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Plot the summary of the simulations.
print(plot(my_sim))

# Obtain the summary plot for the simulation results if DLE and efficacy
# responses are considered in the simulations.

# In the example when no samples are used a data object with doses >= 1
# needs to be defined.
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model of 'ModelEff' (e.g 'Effloglog') class.
eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGain' class.
my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Cohort size of 3.
my_size <- CohortSizeConst(size = 3)

# Stop when 10 subjects are treated (for illustration only).
my_stopping <- StoppingMinPatients(nPatients = 10) | StoppingMissingDose()

## Now specified the design with all the above information and starting with a dose of 25

# Specify the design. (For details please refer to the 'DualResponsesDesign' example.)
my_design <- DualResponsesDesign(
  nextBest = my_next_best,
  model = dle_model,
  eff_model = eff_model,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the true DLE and efficacy curves.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)
my_truth_eff <- efficacyFunction(eff_model, theta1 = -4.818429, theta2 = 3.653058)


# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 1,
  mcmcOptions = McmcOptions(burnin = 10, step = 1, samples = 50),
  seed = 819,
  parallel = FALSE
)

# Summary of the simulations.
my_sum <- summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Plot the summary of the simulations.
print(plot(my_sum))


# Example where DLE and efficacy samples are involved.
# Please refer to design-method 'simulate DualResponsesSamplesDesign' examples
# for details.
# Specify the next best method.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# Specify the design.
my_design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)

# MCMC options.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)


# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 1,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)


# Generate a summary of the simulations.
my_sum <- summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Plot the summary of the simulations.
print(plot(my_sum))


# Example where the 'EffFlexi' class is used for the efficacy model.
eff_model <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = emptydata
)

# Specify the design.
my_design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)

# Specify the true DLE curve and the true expected efficacy values at all dose levels.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)

my_truth_eff <- c(
  -0.5478867, 0.1645417, 0.5248031, 0.7604467,
  0.9333009, 1.0687031, 1.1793942, 1.2726408,
  1.3529598, 1.4233411, 1.4858613, 1.5420182
)

# Define the true gain curve.
my_truth_gain <- function(dose) {
  return((my_truth_eff(dose)) / (1 + (my_truth_dle(dose) / (1 - my_truth_dle(dose)))))
}


## The simulations
## For illustration purpose only 1 simulation is produced (nsim=1).
mySim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueSigma2 = 0.025,
  trueSigma2betaW = 1,
  nsim = 1,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)

# Produce a summary of the simulations.
my_sum <- summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Plot the summary of the simulations.
print(plot(my_sim))

Plot `PseudoSimulationsSummary`

Description

Graphical display of the simulation summary.

This plot method can be applied to PseudoSimulationsSummary objects in order to summarize them graphically. This can be used when only DLE responses are involved in the simulations. This also applied to results with or without samples generated during the simulations.

Usage

## S4 method for signature 'PseudoSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit"),
  ...
)
## S4 method for signature 'PseudoSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit"),
  ...
)

Arguments

x

(PseudoSimulationsSummary)
the object we want to plot from.

y

(missing)
missing object, not used.

type

(character)
the types of plots you want to obtain.

...

not used.

Value

A single ggplot2 object if a single plot is asked for, otherwise a gtable object.

Examples

# nolint start

## obtain the plot for the simulation results
## If only DLE responses are considered in the simulations
## Specified your simulations when no DLE samples are used
## Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid = seq(25, 300, 25))

## Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## Then the escalation rule
tdNextBest <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

## The cohort size, size of 3 subjects
mySize <- CohortSizeConst(size = 3)
## Deifne the increments for the dose-escalation process
## The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
## The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
## This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements <- IncrementsRelative(
  intervals = c(min(data@doseGrid), max(data@doseGrid)),
  increments = c(2, 2)
)
## Specified the stopping rule e.g stop when the maximum sample size of 12 patients has been reached
myStopping <- StoppingMinPatients(nPatients = 12) | StoppingMissingDose()
## Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)

## Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

## Then specified the simulations and generate the trial
## For illustration purpose only 1 simulation is produced (nsim=1).
## The simulations
mySim <- simulate(
  design,
  args = NULL,
  truth = myTruth,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

## Then produce a summary of your simulations
MYSUM <- summary(mySim, truth = myTruth)
## plot the summary of the simulations
print(plot(MYSUM))

## If DLE samples are involved
## The escalation rule
tdNextBest <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)
## specify the design
design <- TDsamplesDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)
## options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## The simulations
## For illustration purpose only 1 simulation is produced (nsim=1).
# mySim <- simulate(design,
#                   args=NULL,
#                   truth=myTruth,
#                   nsim=1,
#                   seed=819,
#                   mcmcOptions=options,
#                   parallel=FALSE)
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
#                  truth=myTruth)
# ##plot the summary of the simulations
# print(plot(MYSUM))

# nolint end
# nolint start

## obtain the plot for the simulation results
## If only DLE responses are considered in the simulations
## Specified your simulations when no DLE samples are used
## Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid = seq(25, 300, 25))

## Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## Then the escalation rule
tdNextBest <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

## The cohort size, size of 3 subjects
mySize <- CohortSizeConst(size = 3)
## Deifne the increments for the dose-escalation process
## The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
## The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
## This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements <- IncrementsRelative(
  intervals = c(min(data@doseGrid), max(data@doseGrid)),
  increments = c(2, 2)
)
## Specified the stopping rule e.g stop when the maximum sample size of 12 patients has been reached
myStopping <- StoppingMinPatients(nPatients = 12) | StoppingMissingDose()
## Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)

## Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

## Then specified the simulations and generate the trial
## For illustration purpose only 1 simulation is produced (nsim=1).
## The simulations
mySim <- simulate(
  design,
  args = NULL,
  truth = myTruth,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

## Then produce a summary of your simulations
MYSUM <- summary(mySim, truth = myTruth)
## plot the summary of the simulations
print(plot(MYSUM))

## If DLE samples are involved
## The escalation rule
tdNextBest <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)
## specify the design
design <- TDsamplesDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)
## options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## The simulations
## For illustration purpose only 1 simulation is produced (nsim=1).
# mySim <- simulate(design,
#                   args=NULL,
#                   truth=myTruth,
#                   nsim=1,
#                   seed=819,
#                   mcmcOptions=options,
#                   parallel=FALSE)
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
#                  truth=myTruth)
# ##plot the summary of the simulations
# print(plot(MYSUM))

# nolint end

Plotting dose-toxicity model fits

Description

Plotting dose-toxicity model fits

Usage

## S4 method for signature 'Samples,DALogisticLogNormal'
plot(x, y, data, hazard = FALSE, ..., showLegend = TRUE)
## S4 method for signature 'Samples,DALogisticLogNormal'
plot(x, y, data, hazard = FALSE, ..., showLegend = TRUE)

Arguments

x

the Samples object

y

the DALogisticLogNormal object

data

the DataDA object

hazard

see fitPEM for the explanation

...

not used

showLegend

should the legend be shown? (default)

Value

This returns the ggplot object for the dose-toxicity model fit

Plotting dose-toxicity and dose-biomarker model fits

Description

When we have the dual endpoint model, also the dose-biomarker fit is shown in the plot

Usage

## S4 method for signature 'Samples,DualEndpoint'
plot(x, y, data, extrapolate = TRUE, showLegend = FALSE, ...)
## S4 method for signature 'Samples,DualEndpoint'
plot(x, y, data, extrapolate = TRUE, showLegend = FALSE, ...)

Arguments

x

the Samples object

y

the DualEndpoint object

data

the DataDual object

extrapolate

should the biomarker fit be extrapolated to the whole dose grid? (default)

showLegend

should the legend be shown? (not default)

...

additional arguments for the parent method plot,Samples,GeneralModel-method

Value

This returns the ggplot object with the dose-toxicity and dose-biomarker model fits

Examples

# nolint start

# Create some data
data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 500)
set.seed(94)
samples <- mcmc(data, model, options)

# Plot the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
#grid.arrange(plot(x = samples, y = model, data = data))

plot(x = samples, y = model, data = data)

# nolint end
# nolint start

# Create some data
data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 500)
set.seed(94)
samples <- mcmc(data, model, options)

# Plot the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
#grid.arrange(plot(x = samples, y = model, data = data))

plot(x = samples, y = model, data = data)

# nolint end

Plotting dose-toxicity model fits

Description

Plotting dose-toxicity model fits

Usage

## S4 method for signature 'Samples,GeneralModel'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Probability of DLT [%]",
  showLegend = TRUE
)
## S4 method for signature 'Samples,GeneralModel'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Probability of DLT [%]",
  showLegend = TRUE
)

Arguments

x

the Samples object

y

the GeneralModel object

data

the Data object

...

not used

xlab

the x axis label

ylab

the y axis label

showLegend

should the legend be shown? (default)

Value

This returns the ggplot object for the dose-toxicity model fit

Examples

# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Plot the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses
plot(x = samples, y = model, data = data)

# nolint end
# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Plot the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses
plot(x = samples, y = model, data = data)

# nolint end

Plot the fitted dose-efficacy curve using a model from `ModelEff` class with samples

Description

Plot the fitted dose-efficacy curve using a model from ModelEff class with samples

Usage

## S4 method for signature 'Samples,ModelEff'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Expected Efficacy",
  showLegend = TRUE
)
## S4 method for signature 'Samples,ModelEff'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Expected Efficacy",
  showLegend = TRUE
)

Arguments

x

the Samples object

y

the ModelEff model class object

data

the Data object

...

not used

xlab

the x axis label

ylab

the y axis label

showLegend

should the legend be shown? (default)

Value

This returns the ggplot object for the dose-efficacy model fit

Examples

# nolint start

## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##plot the dose-efficacy curve with samples using the model from 'ModelEff'
##class e.g. 'Effloglog' class model
##define the model (see Effloglog example)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
## define the samples obtained using the 'Effloglog' model (see details in 'Samples' example)
##options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## samples must be of 'Samples' class
samples <- mcmc(data = data, model = Effmodel, options = options)
## plot the fitted dose-efficacy curve including the 95% credibility interval of the samples
## 'x' should be of 'Samples' class and 'y' of 'ModelEff' class
plot(x = samples, y = Effmodel, data = data)
# nolint end
# nolint start

## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##plot the dose-efficacy curve with samples using the model from 'ModelEff'
##class e.g. 'Effloglog' class model
##define the model (see Effloglog example)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
## define the samples obtained using the 'Effloglog' model (see details in 'Samples' example)
##options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## samples must be of 'Samples' class
samples <- mcmc(data = data, model = Effmodel, options = options)
## plot the fitted dose-efficacy curve including the 95% credibility interval of the samples
## 'x' should be of 'Samples' class and 'y' of 'ModelEff' class
plot(x = samples, y = Effmodel, data = data)
# nolint end

Plot the fitted dose-DLE curve using a `ModelTox` class model with samples

Description

Plot the fitted dose-DLE curve using a ModelTox class model with samples

Usage

## S4 method for signature 'Samples,ModelTox'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Probability of DLT [%]",
  showLegend = TRUE
)
## S4 method for signature 'Samples,ModelTox'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Probability of DLT [%]",
  showLegend = TRUE
)

Arguments

x

the Samples object

y

the ModelTox model class object

data

the Data object

...

not used

xlab

the x axis label

ylab

the y axis label

showLegend

should the legend be shown? (default)

Value

This returns the ggplot object for the dose-DLE model fit

Examples

## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)
## plot the dose-DLE curve with samples using the model from 'ModelTox'
## class e.g. 'LogisticIndepBeta' class model
## define the model (see LogisticIndepBeta example)
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## define the samples obtained using the 'LogisticIndepGBeta' model

## Define options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## (see details in 'Samples' example) samples must be of 'Samples' class
samples <- mcmc(data = data, model = model, options = options)
## plot the fitted dose-DLE curve including the 95% credibility interval of the samples
## 'x' should be of 'Samples' class and 'y' of 'ModelTox' class
plot(x = samples, y = model, data = data)
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)
## plot the dose-DLE curve with samples using the model from 'ModelTox'
## class e.g. 'LogisticIndepBeta' class model
## define the model (see LogisticIndepBeta example)
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## define the samples obtained using the 'LogisticIndepGBeta' model

## Define options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## (see details in 'Samples' example) samples must be of 'Samples' class
samples <- mcmc(data = data, model = model, options = options)
## plot the fitted dose-DLE curve including the 95% credibility interval of the samples
## 'x' should be of 'Samples' class and 'y' of 'ModelTox' class
plot(x = samples, y = model, data = data)

Plot Model-Based Design Simulation Summary

Description

Graphical display of the simulation summary.

This plot method can be applied to SimulationsSummary objects in order to summarize them graphically. Possible type of plots at the moment are those listed in plot,GeneralSimulationsSummary,missing-method plus:

meanFit: Plot showing the average fitted dose-toxicity curve across the trials, together with 95% credible intervals, and comparison with the assumed truth (as specified by the truth argument to summary,Simulations-method)

You can specify any subset of these in the type argument.

Usage

## S4 method for signature 'SimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit"),
  ...
)
## S4 method for signature 'SimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit"),
  ...
)

Arguments

x

(SimulationsSummary)
the object we want to plot from.

y

(missing)
not used.

type

(character)
the types of plots you want to obtain.

...

not used.

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples

# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3 | StoppingMissingDose()

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design
design <- Design(
  model = model,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = emptydata,
  startingDose = 3
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(
  burnin = 5,
  step = 1,
  samples = 10
)
time <- system.time(
  mySims <- simulate(
    design,
    args = NULL,
    truth = myTruth,
    nsim = 1,
    seed = 819,
    mcmcOptions = options,
    parallel = FALSE
  )
)[3]

# Plot the Summary of the Simulations
plot(summary(mySims, truth = myTruth))

# nolint end
# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3 | StoppingMissingDose()

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design
design <- Design(
  model = model,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = emptydata,
  startingDose = 3
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(
  burnin = 5,
  step = 1,
  samples = 10
)
time <- system.time(
  mySims <- simulate(
    design,
    args = NULL,
    truth = myTruth,
    nsim = 1,
    seed = 819,
    mcmcOptions = options,
    parallel = FALSE
  )
)[3]

# Plot the Summary of the Simulations
plot(summary(mySims, truth = myTruth))

# nolint end

Plot `gtable` Objects

Description

This is needed because crmPack uses gridExtra::arrangeGrob() to combine ggplot2 plots, and the resulting gtable object is not plotted otherwise when implicitly printing it in the console.

Usage

## S3 method for class 'gtable'
plot(x, ...)

## S3 method for class 'gtable'
print(x, ...)
## S3 method for class 'gtable'
plot(x, ...)

## S3 method for class 'gtable'
print(x, ...)

Arguments

x

(gtable)
object to print.

...

additional parameters passed to plot.gtable().

Value

Called for side effects.

Plot of the DLE and efficacy curve side by side given a DLE pseudo model, a DLE sample, an efficacy pseudo model and a given efficacy sample

Description

Plot of the DLE and efficacy curve side by side given a DLE pseudo model, a DLE sample, an efficacy pseudo model and a given efficacy sample

Plot of the dose-DLE and dose-efficacy curve side by side given a DLE pseudo model and a given pseudo efficacy model without DLE and efficacy samples

Usage

plotDualResponses(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples'
plotDualResponses(
  DLEmodel,
  DLEsamples,
  Effmodel,
  Effsamples,
  data,
  extrapolate = TRUE,
  showLegend = FALSE,
  ...
)

## S4 method for signature 'ModelTox,missing,ModelEff,missing'
plotDualResponses(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)
plotDualResponses(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples'
plotDualResponses(
  DLEmodel,
  DLEsamples,
  Effmodel,
  Effsamples,
  data,
  extrapolate = TRUE,
  showLegend = FALSE,
  ...
)

## S4 method for signature 'ModelTox,missing,ModelEff,missing'
plotDualResponses(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

Arguments

DLEmodel

the pseudo DLE model of ModelTox class object

DLEsamples

the DLE samples of Samples class object

Effmodel

the pseudo efficacy model of ModelEff class object

Effsamples

the Efficacy samples of Samples class object

data

the data input of DataDual class object

...

additional arguments for the parent method plot,Samples,GeneralModel-method

extrapolate

should the biomarker fit be extrapolated to the whole dose grid? (default)

showLegend

should the legend be shown? (not default)

Value

This returns the ggplot object with the dose-toxicity and dose-efficacy model fits

Functions

plotDualResponses( DLEmodel = ModelTox, DLEsamples = Samples, Effmodel = ModelEff, Effsamples = Samples ): function still to be documented
plotDualResponses( DLEmodel = ModelTox, DLEsamples = missing, Effmodel = ModelEff, Effsamples = missing ): Plot the DLE and efficacy curve side by side given a DLE model and an efficacy model without any samples

Examples

# nolint start

## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##plot the dose-DLE and dose-efficacy curves in two plots with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class
##(e.g 'LogisticIndepBeta' class model)
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
##define the DLE sample of 'Samples' class
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x = data@x, y = data@y, doseGrid = data@doseGrid)
##Specify the options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 1000)


DLEsamples <- mcmc(data = data1, model = DLEmodel, options = options)
##define the efficacy sample of 'Samples' class
Effsamples <- mcmc(data = data, model = Effmodel, options = options)
##plot the dose-DLE and dose-efficacy curves with two plot side by side.
##For each curve the 95% credibility interval of the two samples are alos given
plotDualResponses(
  DLEmodel = DLEmodel,
  DLEsamples = DLEsamples,
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  data = data
)

# nolint end
# nolint start

## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##plot the dose-DLE and dose-efficacy curves in two plots without DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class
##(e.g 'LogisticIndepBeta' class model)
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
##plot the dose-DLE and dose-efficacy curves with two plot side by side.
plotDualResponses(DLEmodel = DLEmodel, Effmodel = Effmodel, data = data)

# nolint end
# nolint start

## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##plot the dose-DLE and dose-efficacy curves in two plots with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class
##(e.g 'LogisticIndepBeta' class model)
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
##define the DLE sample of 'Samples' class
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x = data@x, y = data@y, doseGrid = data@doseGrid)
##Specify the options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 1000)


DLEsamples <- mcmc(data = data1, model = DLEmodel, options = options)
##define the efficacy sample of 'Samples' class
Effsamples <- mcmc(data = data, model = Effmodel, options = options)
##plot the dose-DLE and dose-efficacy curves with two plot side by side.
##For each curve the 95% credibility interval of the two samples are alos given
plotDualResponses(
  DLEmodel = DLEmodel,
  DLEsamples = DLEsamples,
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  data = data
)

# nolint end
# nolint start

## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##plot the dose-DLE and dose-efficacy curves in two plots without DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class
##(e.g 'LogisticIndepBeta' class model)
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
##plot the dose-DLE and dose-efficacy curves with two plot side by side.
plotDualResponses(DLEmodel = DLEmodel, Effmodel = Effmodel, data = data)

# nolint end

Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, a DLE sample, a given efficacy pseudo model and an efficacy sample

Description

Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, a DLE sample, a given efficacy pseudo model and an efficacy sample

Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, and a given efficacy pseudo model

Usage

plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples'
plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,missing,ModelEff,missing'
plotGain(DLEmodel, Effmodel, data, size = c(8L, 8L), shape = c(16L, 17L), ...)
plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples'
plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,missing,ModelEff,missing'
plotGain(DLEmodel, Effmodel, data, size = c(8L, 8L), shape = c(16L, 17L), ...)

Arguments

DLEmodel

the dose-DLE model of ModelTox class object

DLEsamples

the DLE sample of Samples class object

Effmodel

the dose-efficacy model of ModelEff class object

Effsamples

the efficacy sample of of Samples class object

data

the data input of DataDual class object

...

not used

size

(integer)
a vector of length two defining the sizes of the shapes used to identify the doses with, respectively, p(DLE = 0.3) and the maximum gain

shape

(integer)
a vector of length two defining the shapes used to identify the doses with, respectively, p(DLE = 0.3) and the maximum gain

Value

This returns the ggplot object for the plot

Functions

plotGain( DLEmodel = ModelTox, DLEsamples = Samples, Effmodel = ModelEff, Effsamples = Samples ): Standard method
plotGain( DLEmodel = ModelTox, DLEsamples = missing, Effmodel = ModelEff, Effsamples = missing ): Standard method

Examples

# nolint start

## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##plot the dose-DLE , dose-efficacy and gain curve in the same plot with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class
##(e.g 'LogisticIndepBeta' class model)
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  const = 0
)
##define the DLE sample of 'Samples' class
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x = data@x, y = data@y, doseGrid = data@doseGrid)

##Define the options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 1000)


DLEsamples <- mcmc(data = data1, model = DLEmodel, options = options)
##define the efficacy sample of 'Samples' class
Effsamples <- mcmc(data = data, model = Effmodel, options = options)
##plot the three curves of mean values of the DLEsamples, Effsamples and
##gain value samples (obtained within this plotGain function) at all dose levels
plotGain(
  DLEmodel = DLEmodel,
  DLEsamples = DLEsamples,
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  data = data
)
# nolint end
# nolint start
## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##plot the dose-DLE , dose-efficacy and gain curve in the same plot with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class
##(e.g 'LogisticIndepBeta' class model)
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
##plot the three curves of using modal estimates of model parameters at all dose levels
plotGain(DLEmodel = DLEmodel, Effmodel = Effmodel, data = data)
# nolint end
# nolint start

## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##plot the dose-DLE , dose-efficacy and gain curve in the same plot with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class
##(e.g 'LogisticIndepBeta' class model)
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  const = 0
)
##define the DLE sample of 'Samples' class
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x = data@x, y = data@y, doseGrid = data@doseGrid)

##Define the options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 1000)


DLEsamples <- mcmc(data = data1, model = DLEmodel, options = options)
##define the efficacy sample of 'Samples' class
Effsamples <- mcmc(data = data, model = Effmodel, options = options)
##plot the three curves of mean values of the DLEsamples, Effsamples and
##gain value samples (obtained within this plotGain function) at all dose levels
plotGain(
  DLEmodel = DLEmodel,
  DLEsamples = DLEsamples,
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  data = data
)
# nolint end
# nolint start
## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
##plot the dose-DLE , dose-efficacy and gain curve in the same plot with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class
##(e.g 'LogisticIndepBeta' class model)
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
##plot the three curves of using modal estimates of model parameters at all dose levels
plotGain(DLEmodel = DLEmodel, Effmodel = Effmodel, data = data)
# nolint end

`positive_number`

Description

The positive_number class is a class to store not NULL, non NA, finite and strictly positive numerical value. It is mainly used to store reference dose value in model classes.

Computing Toxicity Probabilities for a Given Dose, Model and Samples

Description

A function that computes the probability of the occurrence of a DLE at a specified dose level, based on the model parameters (samples).

Usage

prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticNormal,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticLogNormal,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticLogNormalSub,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,ProbitLogNormal,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,ProbitLogNormalRel,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticLogNormalGrouped,Samples'
prob(dose, model, samples, group, ...)

## S4 method for signature 'numeric,LogisticKadane,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticKadaneBetaGamma,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticNormalMixture,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticNormalFixedMixture,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticLogNormalMixture,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,DualEndpoint,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticIndepBeta,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticIndepBeta,missing'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,OneParLogNormalPrior,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,OneParExpPrior,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticLogNormalOrdinal,Samples'
prob(dose, model, samples, grade, cumulative = TRUE, ...)
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticNormal,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticLogNormal,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticLogNormalSub,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,ProbitLogNormal,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,ProbitLogNormalRel,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticLogNormalGrouped,Samples'
prob(dose, model, samples, group, ...)

## S4 method for signature 'numeric,LogisticKadane,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticKadaneBetaGamma,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticNormalMixture,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticNormalFixedMixture,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticLogNormalMixture,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,DualEndpoint,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticIndepBeta,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticIndepBeta,missing'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,OneParLogNormalPrior,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,OneParExpPrior,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,LogisticLogNormalOrdinal,Samples'
prob(dose, model, samples, grade, cumulative = TRUE, ...)

Arguments

dose

(number or numeric)
the dose which is targeted. The following recycling rule applies when samples is not missing: vectors of size 1 will be recycled to the size of the sample (i.e. size(samples)). Otherwise, dose must have the same size as the sample.

model

(GeneralModel or ModelTox)
the model for single agent dose escalation or pseudo DLE (dose-limiting events)/toxicity model.

samples

(Samples)
the samples of model's parameters that will be used to compute toxicity probabilities. Can also be missing for some models.

...

model specific parameters when samples are not used.

group

(character or factor)
for LogisticLogNormalGrouped, indicating whether to calculate the probability for the mono or for the combo arm.

grade

(integer or integer_vector)
The toxicity grade for which probabilities are required

cumulative

(flag)
Should the returned probability be cumulative (the default) or grade-specific?

Details

The prob() function computes the probability of toxicity for given doses, using samples of the model parameter(s). If you work with multivariate model parameters, then assume that your model specific prob() method receives a samples matrix where the rows correspond to the sampling index, i.e. the layout is then ⁠nSamples x dimParameter⁠.

Value

A proportion or numeric vector with the toxicity probabilities. If non-scalar samples were used, then every element in the returned vector corresponds to one element of a sample. Hence, in this case, the output vector is of the same length as the sample vector. If scalar samples were used or no samples were used, e.g. for pseudo DLE/toxicity model, then the output is of the same length as the length of the dose. In the case of LogisticLogNormalOrdinal, the probabilities relate to toxicities of grade given by grade.

Functions

prob(dose = numeric, model = LogisticNormal, samples = Samples):
prob(dose = numeric, model = LogisticLogNormal, samples = Samples):
prob(dose = numeric, model = LogisticLogNormalSub, samples = Samples):
prob(dose = numeric, model = ProbitLogNormal, samples = Samples):
prob(dose = numeric, model = ProbitLogNormalRel, samples = Samples):
prob(dose = numeric, model = LogisticLogNormalGrouped, samples = Samples): method for LogisticLogNormalGrouped which needs group argument in addition.
prob(dose = numeric, model = LogisticKadane, samples = Samples):
prob(dose = numeric, model = LogisticKadaneBetaGamma, samples = Samples):
prob(dose = numeric, model = LogisticNormalMixture, samples = Samples):
prob(dose = numeric, model = LogisticNormalFixedMixture, samples = Samples):
prob(dose = numeric, model = LogisticLogNormalMixture, samples = Samples):
prob(dose = numeric, model = DualEndpoint, samples = Samples):
prob(dose = numeric, model = LogisticIndepBeta, samples = Samples): compute toxicity probabilities of the occurrence of a DLE at a specified dose level, based on the samples of LogisticIndepBeta model parameters.
prob(dose = numeric, model = LogisticIndepBeta, samples = missing): compute toxicity probabilities of the occurrence of a DLE at a specified dose level, based on the LogisticIndepBeta model parameters. All model parameters (except dose) should be present in the model object.
prob(dose = numeric, model = OneParLogNormalPrior, samples = Samples):
prob(dose = numeric, model = OneParExpPrior, samples = Samples):
prob(dose = numeric, model = LogisticLogNormalOrdinal, samples = Samples):

Note

The prob() and dose() functions are the inverse of each other, for all dose() methods for which its first argument, i.e. a given independent variable that dose depends on, represents toxicity probability.

Examples

# Create some data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model, e.g. 'LogisticLogNormal'.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get samples from posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 20)
my_samples <- mcmc(data = my_data, model = my_model, options = my_options)

# Posterior for Prob(DLT | dose = 50).
prob(dose = 50, model = my_model, samples = my_samples)

# Create data from the 'DataDual' class.
data_dual <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

# Initialize a toxicity model using 'LogisticIndepBeta' model.
dlt_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data_dual
)

# Get samples from posterior.
dlt_sample <- mcmc(data = data_dual, model = dlt_model, options = my_options)

# Posterior for Prob(DLT | dose = 100).
prob(dose = 100, model = dlt_model, samples = dlt_sample)
prob(dose = c(50, 150), model = dlt_model)
# Create some data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize a model, e.g. 'LogisticLogNormal'.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Get samples from posterior.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 20)
my_samples <- mcmc(data = my_data, model = my_model, options = my_options)

# Posterior for Prob(DLT | dose = 50).
prob(dose = 50, model = my_model, samples = my_samples)

# Create data from the 'DataDual' class.
data_dual <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

# Initialize a toxicity model using 'LogisticIndepBeta' model.
dlt_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data_dual
)

# Get samples from posterior.
dlt_sample <- mcmc(data = data_dual, model = dlt_model, options = my_options)

# Posterior for Prob(DLT | dose = 100).
prob(dose = 100, model = dlt_model, samples = dlt_sample)
prob(dose = c(50, 150), model = dlt_model)

Getting the Prob Function for a Given Model Type

Description

A function that returns a prob() function that computes the toxicity probabilities for a given dose level, based on the model specific parameters.

Usage

probFunction(model, ...)

## S4 method for signature 'GeneralModel'
probFunction(model, ...)

## S4 method for signature 'ModelTox'
probFunction(model, ...)

## S4 method for signature 'LogisticLogNormalOrdinal'
probFunction(model, grade, ...)
probFunction(model, ...)

## S4 method for signature 'GeneralModel'
probFunction(model, ...)

## S4 method for signature 'ModelTox'
probFunction(model, ...)

## S4 method for signature 'LogisticLogNormalOrdinal'
probFunction(model, grade, ...)

Arguments

model

(GeneralModel or ModelTox)
the model.

...

model specific parameters.

grade

(integer)
the toxicity grade for which the dose function is required

Value

A prob() function that computes toxicity probabilities.

Functions

probFunction(GeneralModel):
probFunction(ModelTox):
probFunction(LogisticLogNormalOrdinal):

Examples

my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

prob_fun <- probFunction(my_model, alpha0 = 2, alpha1 = 3)
prob_fun(30)
ordinal_data <- .DefaultDataOrdinal()
ordinal_model <- .DefaultLogisticLogNormalOrdinal()
opts <- .DefaultMcmcOptions()
samples <- mcmc(ordinal_data, ordinal_model, opts)

probFunction(
  ordinal_model,
  grade = 2L,
  alpha2 = samples@data$alpha1,
  beta = samples@data$beta
)(50)
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

prob_fun <- probFunction(my_model, alpha0 = 2, alpha1 = 3)
prob_fun(30)
ordinal_data <- .DefaultDataOrdinal()
ordinal_model <- .DefaultLogisticLogNormalOrdinal()
opts <- .DefaultMcmcOptions()
samples <- mcmc(ordinal_data, ordinal_model, opts)

probFunction(
  ordinal_model,
  grade = 2L,
  alpha2 = samples@data$alpha1,
  beta = samples@data$beta
)(50)

Shorthand for Probit Function

Description

Computes the probit (inverse cumulative distribution function of standard normal) transformation.

Usage

probit(x)
probit(x)

Arguments

x

(numeric)
the function argument.

Value

The probit of x, the quantile function of the standard normal distribution.

Examples

probit(0.5)
probit(c(0.025, 0.5, 0.975))
probit(0.5)
probit(c(0.025, 0.5, 0.975))

`ProbitLogNormal`

Description

ProbitLogNormal is the class for probit regression model with a bivariate normal prior on the intercept and log slope.

Usage

ProbitLogNormal(mean, cov, ref_dose = 1)

.DefaultProbitLogNormal()
ProbitLogNormal(mean, cov, ref_dose = 1)

.DefaultProbitLogNormal()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose $x*$ (strictly positive number).

Details

The covariate is the natural logarithm of dose $x$ divided by a reference dose $x*$ , i.e.:

$probit[p(x)] = alpha0 + alpha1 * log(x/x*),$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ . The prior

$(alpha0, log(alpha1)) ~ Normal(mean, cov).$

Note

The model used in the DualEndpoint classes is an extension of this model: DualEndpoint supports both ProbitNormal (which is not implemented yet) and ProbitLogNormal models through its use_log_dose slot. ProbitLogNormal has no such flag, so always uses ⁠log(x/x*)⁠as a covariate in its model. Therefore this class can be used to check the prior assumptions on the dose-toxicity model, even when sampling from the prior distribution of the dual endpoint model is not possible, when use_log_dose = TRUE is used.

Typically, end users will not use the .DefaultProbitLogNormal() function.

Examples

my_model <- ProbitLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 7.2
)
my_model <- ProbitLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 7.2
)

`ProbitLogNormalRel`

Description

ProbitLogNormalRel is the class for probit regression model with a bivariate normal prior on the intercept and log slope.

Usage

ProbitLogNormalRel(mean, cov, ref_dose = 1)

.DefaultProbitLogNormalRel()
ProbitLogNormalRel(mean, cov, ref_dose = 1)

.DefaultProbitLogNormalRel()

Arguments

mean

(numeric)
the prior mean vector.

cov

(matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.

ref_dose

(number)
the reference dose $x*$ (strictly positive number).

Details

The covariate is the dose $x$ divided by a reference dose $x*$ , i.e.:

$probit[p(x)] = alpha0 + alpha1 * x/x*,$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ . The prior

$(alpha0, log(alpha1)) ~ Normal(mean, cov).$

Note

This model is also used in the DualEndpoint classes, so this class can be used to check the prior assumptions on the dose-toxicity model, even when sampling from the prior distribution of the dual endpoint model is not possible.

Typically, end users will not use the .DefaultProbitLogNormalRel() function.

Examples

my_model <- ProbitLogNormalRel(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
)
my_model <- ProbitLogNormalRel(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2)
)

`PseudoDualFlexiSimulations`

Description

This class captures the trial simulations design using both the DLE and efficacy responses using EffFlexi efficacy model. It extends PseudoDualSimulations by adding the capability to capture the sigma2betaW estimates.

Usage

PseudoDualFlexiSimulations(sigma2_beta_w_est, ...)

.DefaultPseudoDualFlexiSimulations()
PseudoDualFlexiSimulations(sigma2_beta_w_est, ...)

.DefaultPseudoDualFlexiSimulations()

Arguments

sigma2_beta_w_est

(numeric)
the vector of the final posterior mean sigma2betaW estimates

...

additional parameters from PseudoDualSimulations

Slots

sigma2_beta_w_est: (numeric)
the vector of the final posterior mean sigma2betaW estimates

Note

Typically, end users will not use the .DefaultPseudoFlexiSimulations() function.

`PseudoDualSimulations`

Description

This class conducts trial simulations for designs using both the DLE and efficacy responses. It defines final values for efficacy fit and DLE, estimates of Gstar, optimal dose and sigma2.

Usage

PseudoDualSimulations(
  fit_eff,
  final_gstar_estimates,
  final_gstar_at_dose_grid,
  final_gstar_cis,
  final_gstar_ratios,
  final_optimal_dose,
  final_optimal_dose_at_dose_grid,
  sigma2_est,
  ...
)

.DefaultPseudoDualSimulations()
PseudoDualSimulations(
  fit_eff,
  final_gstar_estimates,
  final_gstar_at_dose_grid,
  final_gstar_cis,
  final_gstar_ratios,
  final_optimal_dose,
  final_optimal_dose_at_dose_grid,
  sigma2_est,
  ...
)

.DefaultPseudoDualSimulations()

Arguments

fit_eff

(list)
see slot definition.

final_gstar_estimates

(numeric)
see slot definition.

final_gstar_at_dose_grid

(numeric)
see slot definition.

final_gstar_cis

(list)
see slot definition.

final_gstar_ratios

(numeric)
see slot definition.

final_optimal_dose

(numeric)
see slot definition.

final_optimal_dose_at_dose_grid

(numeric)
see slot definition.

sigma2_est

(numeric)
see slot definition.

...

additional parameters from PseudoSimulations

Slots

fit_eff: (list)
final values of efficacy fit.
final_gstar_estimates: (numeric)
final Gstar estimates.
final_gstar_at_dose_grid: (numeric)
final Gstar estimates at dose grid.
final_gstar_cis: (list)
list of 95% confidence interval for Gstar estimates.
final_gstar_ratios: (numeric)
ratios of confidence intervals for Gstar estimates.
final_optimal_dose: (numeric)
final optimal dose.
final_optimal_dose_at_dose_grid: (numeric)
final optimal dose at dose grid.
sigma2_est: (numeric)
final sigma2 estimates.

Note

Do not use the .DefaultPseudoDualSimulations() function.

`PseudoDualSimulationsSummary`

Description

This class captures the summary of the dual responses simulations using pseudo models. It contains all slots from PseudoSimulationsSummary object. In addition to the slots in the parent class PseudoSimulationsSummary, it contains additional slots for the efficacy model fit information.

Note that objects should not be created by users, therefore no initialization function is provided for this class.

Usage

.DefaultPseudoDualSimulationsSummary()
.DefaultPseudoDualSimulationsSummary()

Slots

target_gstar: (numeric)
the target dose level such that its gain value is at maximum
target_gstar_at_dose_grid: (numeric)
the dose level at dose Grid closest and below Gstar
gstar_summary: (table)
the six-number table summary (lowest, 25th, 50th (median), 75th percentile, mean and highest value) of the final Gstar values obtained across all simulations
ratio_gstar_summary: (table)
the six-number summary table of the ratios of the upper to the lower 95% credibility intervals of the final Gstar across all simulations
eff_fit_at_dose_most_selected: (numeric)
fitted expected mean efficacy value at dose most often selected
mean_eff_fit: (list)
list with mean, lower (2.5%) and upper (97.5%) quantiles of the fitted expected efficacy value at each dose level.

Note

Typically, end users will not use the .DefaultPseudoDualSimulationsSummary() function.

`PseudoSimulations`

Description

This class captures trial simulations from designs using pseudo model. It has additional slots fit and stop_reasons compared to the general class GeneralSimulations.

Usage

PseudoSimulations(
  fit,
  final_td_target_during_trial_estimates,
  final_td_target_end_of_trial_estimates,
  final_td_target_during_trial_at_dose_grid,
  final_td_target_end_of_trial_at_dose_grid,
  final_tdeot_cis,
  final_tdeot_ratios,
  final_cis,
  final_ratios,
  stop_report,
  stop_reasons,
  ...
)

.DefaultPseudoSimulations()
PseudoSimulations(
  fit,
  final_td_target_during_trial_estimates,
  final_td_target_end_of_trial_estimates,
  final_td_target_during_trial_at_dose_grid,
  final_td_target_end_of_trial_at_dose_grid,
  final_tdeot_cis,
  final_tdeot_ratios,
  final_cis,
  final_ratios,
  stop_report,
  stop_reasons,
  ...
)

.DefaultPseudoSimulations()

Arguments

fit

(list)
see slot definition.

final_td_target_during_trial_estimates

(numeric)
see slot definition.

final_td_target_end_of_trial_estimates

(numeric)
see slot definition.

final_td_target_during_trial_at_dose_grid

(numeric)
see slot definition.

final_td_target_end_of_trial_at_dose_grid

(numeric)
see slot definition.

final_tdeot_cis

(list)
see slot definition.

final_tdeot_ratios

(numeric)
see slot definition.

final_cis

(list)
see slot definition.

final_ratios

(numeric)
see slot definition.

stop_report

see PseudoSimulations

stop_reasons

(list)
see slot definition.

...

additional parameters from GeneralSimulations

Slots

fit: (list)
final fit values.
final_td_target_during_trial_estimates: (numeric)
final estimates of the td_target_during_trial.
final_td_target_end_of_trial_estimates: (numeric)
final estimates of the td_target_end_of_trial.
final_td_target_during_trial_at_dose_grid: (numeric)
dose levels at dose grid closest below the final td_target_during_trial estimates.
final_td_target_end_of_trial_at_dose_grid: (numeric)
dose levels at dose grid closest below the final td_target_end_of_trial estimates.
final_tdeot_cis: (list)
95% credibility intervals of the final estimates for td_target_end_of_trial.
final_tdeot_ratios: (numeric)
ratio of the upper to the lower 95% credibility intervals for td_target_end_of_trial.
final_cis: (list)
final 95% credibility intervals for td_target_end_of_trial estimates.
final_ratios: (numeric)
final ratios of the upper to the lower 95% credibility interval for td_target_end_of_trial.
stop_report: (matrix)
outcomes of stopping rules.
stop_reasons: (list)
reasons for stopping each simulation run.

Note

Typically, end users will not use the .DefaultPseudoSimulations() function.

`PseudoSimulationsSummary`

Description

This class captures the summary of pseudo-models simulations output. Note that objects should not be created by users, therefore no initialization function is provided for this class.

Usage

.DefaultPseudoSimulationsSummary()
.DefaultPseudoSimulationsSummary()

Slots

target_end_of_trial: (numeric)
the target probability of DLE wanted at the end of a trial
target_dose_end_of_trial: (numeric)
the dose level corresponds to the target probability of DLE wanted at the end of a trial, TDEOT
target_dose_end_of_trial_at_dose_grid: (numeric)
the dose level at dose grid corresponds to the target probability of DLE wanted at the end of a trial
target_during_trial: (numeric)
the target probability of DLE wanted during a trial
target_dose_during_trial: (numeric)
the dose level corresponds to the target probability of DLE wanted during the trial. TDDT
target_dose_during_trial_at_dose_grid: (numeric)
the dose level at dose grid corresponds to the target probability of DLE wanted during a trial
tdeot_summary: (table)
the six-number table summary, include the lowest, the 25th percentile (lower quartile), the 50th percentile (median), the mean, the 75th percentile and the highest values of the final dose levels obtained corresponds to the target probability of DLE want at the end of a trial across all simulations
tddt_summary: (table)
the six-number table summary, include the lowest, the 25th percentile (lower quartile), the 50th percentile (median), the mean, the 75th percentile and the highest values of the final dose levels obtained corresponds to the target probability of DLE want during a trial across all simulations
final_dose_rec_summary: (table)
the six-number table summary, include the lowest, the 25th percentile (lower quartile), the 50th percentile (median), the mean, the 75th percentile and the highest values of the final optimal doses, which is either the TDEOT when only DLE response are incorporated into the escalation procedure or the minimum of the TDEOT and Gstar when DLE and efficacy responses are incorporated, across all simulations
ratio_tdeot_summary: (table)
the six-number summary table of the final ratios of the upper to the lower 95% credibility intervals of the final TDEOTs across all simulations
final_ratio_summary: (table)
the six-number summary table of the final ratios of the upper to the lower 95% credibility intervals of the final optimal doses across all simulations
nsim: (integer)
number of simulations
prop_dle: (numeric)
proportions of DLE in the trials
mean_tox_risk: (numeric)
mean toxicity risks for the patients
dose_selected: (numeric)
doses selected as MTD (target_dose_end_of_trial)
tox_at_doses_selected: (numeric)
true toxicity at doses selected
prop_at_target_end_of_trial: (numeric)
Proportion of trials selecting at the dose_grid closest below the MTD, the target_dose_end_of_trial
prop_at_target_during_trial: (numeric)
Proportion of trials selecting at the dose_grid closest below the target_dose_during_trial
dose_most_selected: (numeric)
dose most often selected as MTD
obs_tox_rate_at_dose_most_selected: (numeric)
observed toxicity rate at dose most often selected
n_obs: (integer)
number of patients overall
n_above_target_end_of_trial: (integer)
number of patients treated above target_dose_end_of_trial
n_above_target_during_trial: (integer)
number of patients treated above target_dose_during_trial
dose_grid: (numeric)
the dose grid that has been used
fit_at_dose_most_selected: (numeric)
fitted toxicity rate at dose most often selected
mean_fit: (list)
list with the average, lower (2.5%) and upper (97.5%) quantiles of the mean fitted toxicity at each dose level
stop_report: (matrix)
matrix of stopping rule outcomes

Note

Typically, end users will not use the .DefaultPseudoSimulationsSummary() function.

Convert Prior Quantiles to Logistic (Log) Normal Model

Description

This function uses generalized simulated annealing to optimize a LogisticNormal model to be as close as possible to the given prior quantiles.

Usage

Quantiles2LogisticNormal(
  dosegrid,
  refDose,
  lower,
  median,
  upper,
  level = 0.95,
  logNormal = FALSE,
  parstart = NULL,
  parlower = c(-10, -10, 0, 0, -0.95),
  parupper = c(10, 10, 10, 10, 0.95),
  seed = 12345,
  verbose = TRUE,
  control = list(threshold.stop = 0.01, maxit = 50000, temperature = 50000, max.time =
    600)
)
Quantiles2LogisticNormal(
  dosegrid,
  refDose,
  lower,
  median,
  upper,
  level = 0.95,
  logNormal = FALSE,
  parstart = NULL,
  parlower = c(-10, -10, 0, 0, -0.95),
  parupper = c(10, 10, 10, 10, 0.95),
  seed = 12345,
  verbose = TRUE,
  control = list(threshold.stop = 0.01, maxit = 50000, temperature = 50000, max.time =
    600)
)

Arguments

dosegrid

(numeric)
the dose grid.

refDose

(number)
the reference dose.

lower

(numeric)
the lower quantiles.

median

(numeric)
the medians.

upper

(numeric)
the upper quantiles.

level

(number)
the credible level of the (lower, upper) intervals. Default is 0.95.

logNormal

(flag)
use the log-normal prior? If FALSE (default), the normal prior for the logistic regression coefficients is used.

parstart

(numeric or NULL)
starting values for the parameters. By default, these are determined from the medians supplied.

parlower

(numeric)
lower bounds on the parameters (intercept alpha and the slope beta, the corresponding standard deviations and the correlation).

parupper

(numeric)
upper bounds on the parameters.

seed

(count)
seed for random number generation.

verbose

(flag)
should the function be verbose?

control

(list)
additional options for the optimisation routine, see GenSA::GenSA() for more details.

Value

A list with the best approximating model (LogisticNormal or LogisticLogNormal), the resulting quantiles, the required quantiles and the distance to the required quantiles, as well as the final parameters (which could be used for running the algorithm a second time).

`Recruitment`

Description

Recruitment is a virtual class for recruitment criteria, from which all other specific recruitment criteria classes inherit. The subclasses are used to specify the maximum number of backfill patients that can be recruited relative to the main trial cohort size.

Usage

.DefaultRecruitment()
.DefaultRecruitment()

Note

Typically, end users will not use the .DefaultRecruitment() function.

`RecruitmentRatio`

Description

RecruitmentRatio constrains the recruitment of backfill patients based on a ratio to the main trial cohort size. The maximum number of backfill patients is calculated as ceiling(ratio * active_cohort_size).

Usage

RecruitmentRatio(ratio = 1)

.DefaultRecruitmentRatio()
RecruitmentRatio(ratio = 1)

.DefaultRecruitmentRatio()

Arguments

ratio

(number) see slot definition.

Slots

ratio: (number) the recruitment ratio, specifying the maximum number of backfill patients per patient in the main trial cohort (non-negative).

Note

Typically, end users will not use the .DefaultRecruitmentRatio() function.

Examples

# Create a RecruitmentRatio object with ratio 0.5
# This means 1 backfill patient for every 2 patients in the active cohort
recruitment <- RecruitmentRatio(ratio = 0.5)
print(recruitment)

# Create a variant with ratio 1 (1:1)
recruitment_one_to_one <- RecruitmentRatio(ratio = 1)
print(recruitment_one_to_one)
# Create a RecruitmentRatio object with ratio 0.5
# This means 1 backfill patient for every 2 patients in the active cohort
recruitment <- RecruitmentRatio(ratio = 0.5)
print(recruitment)

# Create a variant with ratio 1 (1:1)
recruitment_one_to_one <- RecruitmentRatio(ratio = 1)
print(recruitment_one_to_one)

`RecruitmentUnlimited`

Description

RecruitmentUnlimited allows unlimited recruitment of backfill patients. There is no constraint on the number of backfill patients relative to the main trial cohort size.

Usage

RecruitmentUnlimited()

.DefaultRecruitmentUnlimited()
RecruitmentUnlimited()

.DefaultRecruitmentUnlimited()

Note

Typically, end users will not use the .DefaultRecruitmentUnlimited() function.

Examples

# Create a RecruitmentUnlimited object
recruitment <- RecruitmentUnlimited()
print(recruitment)

# Calculate maximum recruits for an active cohort of size 10
max_recruits <- maxRecruits(recruitment, active_cohort_size = 10)
print(max_recruits) # Practically unlimited (1e6)
# Create a RecruitmentUnlimited object
recruitment <- RecruitmentUnlimited()
print(recruitment)

# Calculate maximum recruits for an active cohort of size 10
max_recruits <- maxRecruits(recruitment, active_cohort_size = 10)
print(max_recruits) # Practically unlimited (1e6)

`RuleDesign`

Description

RuleDesign is the class for rule-based designs. The difference between this class and the Design class is that RuleDesign does not contain model, stopping and increments slots.

Usage

RuleDesign(nextBest, cohort_size, data, startingDose)

.DefaultRuleDesign()

ThreePlusThreeDesign(doseGrid)
RuleDesign(nextBest, cohort_size, data, startingDose)

.DefaultRuleDesign()

ThreePlusThreeDesign(doseGrid)

Arguments

nextBest

(NextBest)
see slot definition.

cohort_size

(CohortSize)
see slot definition.

data

(Data)
see slot definition.

startingDose

(number)
see slot definition.

doseGrid

(numeric)
the dose grid to be used (sorted).

Functions

ThreePlusThreeDesign(): creates a new 3+3 design object from a dose grid.

Slots

nextBest: (NextBest)
how to find the next best dose.
cohort_size: (CohortSize)
rules for the cohort sizes.
data: (Data)
specifies dose grid, any previous data, etc.
startingDose: (number)
the starting dose, it must lie on the dose grid in data.

Note

Typically, end users will not use the .DefaultRuleDesign() function.

Examples

# Specify the design to run simulations. The design comprises a model,
# the escalation rule, starting data, a cohort size and a starting dose.

# Initialing a 3+3 design with constant cohort size of 3 and starting dose equal 5.
my_design <- RuleDesign(
  nextBest = NextBestThreePlusThree(),
  cohort_size = CohortSizeConst(size = 3L),
  data = Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80)),
  startingDose = 5
)
# Initialing a 3+3 design with constant cohort size of 3 and starting dose equal 8.
my_design <- ThreePlusThreeDesign(doseGrid = c(8, 10, 15, 25, 35, 50, 80))
# Specify the design to run simulations. The design comprises a model,
# the escalation rule, starting data, a cohort size and a starting dose.

# Initialing a 3+3 design with constant cohort size of 3 and starting dose equal 5.
my_design <- RuleDesign(
  nextBest = NextBestThreePlusThree(),
  cohort_size = CohortSizeConst(size = 3L),
  data = Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80)),
  startingDose = 5
)
# Initialing a 3+3 design with constant cohort size of 3 and starting dose equal 8.
my_design <- ThreePlusThreeDesign(doseGrid = c(8, 10, 15, 25, 35, 50, 80))

`RuleDesignOrdinal`

Description

RuleDesignOrdinal is the class for rule-based designs. The difference between this class and the DesignOrdinal class is that RuleDesignOrdinal does not contain model, stopping and increments slots.

Usage

RuleDesignOrdinal(next_best, cohort_size, data, starting_dose)

.DefaultRuleDesignOrdinal()
RuleDesignOrdinal(next_best, cohort_size, data, starting_dose)

.DefaultRuleDesignOrdinal()

Arguments

next_best

(NextBestOrdinal)
see slot definition.

cohort_size

(CohortSize)
see slot definition.

data

(DataOrdinal)
see slot definition.

starting_dose

(number)
see slot definition.

Details

Please note that the cohort size rules need to be wrapped into the corresponding CohortSizeOrdinal class, before a successful evaluation of the corresponding methods can take place. Note also that these wrappers cannot be nested, i.e., you cannot have a CohortSizeOrdinal inside another CohortSizeOrdinal (which also would not make sense) because it would not be clear which event grade to use for the methods calculation. However, multiple rules can be combined using the operators defined, e.g., CohortSizeMin(list(CohortSizeOrdinal(1L, rule1), CohortSizeOrdinal(2L, rule2))).

Slots

next_best: (NextBestOrdinal)
how to find the next best dose.
cohort_size: (CohortSize)
rules for the cohort sizes.
data: (DataOrdinal)
specifies dose grid, any previous data, etc.
starting_dose: (number)
the starting dose, it must lie on the dose grid in data.

Note

Typically, end users will not use the .DefaultRuleDesignOrdinal() function.

Examples

RuleDesignOrdinal(
  next_best = NextBestOrdinal(
    1L,
    NextBestMTD(
      target = 0.25,
      derive = function(x) median(x, na.rm = TRUE)
    )
  ),
  cohort_size = CohortSizeOrdinal(1L, CohortSizeConst(size = 3L)),
  data = DataOrdinal(doseGrid = c(5, 10, 15, 25, 35, 50, 80)),
  starting_dose = 5
)
RuleDesignOrdinal(
  next_best = NextBestOrdinal(
    1L,
    NextBestMTD(
      target = 0.25,
      derive = function(x) median(x, na.rm = TRUE)
    )
  ),
  cohort_size = CohortSizeOrdinal(1L, CohortSizeConst(size = 3L)),
  data = DataOrdinal(doseGrid = c(5, 10, 15, 25, 35, 50, 80)),
  starting_dose = 5
)

`SafetyWindow`

Description

SafetyWindow is a class for safety window.

Usage

.DefaultSafetyWindow()
.DefaultSafetyWindow()

Note

Typically, end users will not use the .DefaultSafetyWindow() function.

`SafetyWindowConst`

Description

SafetyWindowConst is the class for safety window length and it is used when the gap should be kept constant across cohorts (though it may vary within a cohort).

Usage

SafetyWindowConst(gap, follow, follow_min)

.DefaultSafetyWindowConst()
SafetyWindowConst(gap, follow, follow_min)

.DefaultSafetyWindowConst()

Arguments

gap

see slot definition.

follow

see slot definition.

follow_min

see slot definition.

Slots

gap: (integer)
a vector, the constant gap between patients.
follow: (count)
how long to follow each patient. The period of time that each patient in the cohort needs to be followed before the next cohort opens.
follow_min: (count)
minimum follow up. At least one patient in the cohort needs to be followed at the minimal follow up time.

Note

Typically, end users will not use the .DefaultSafetyWindowConst() function.

Examples

# This is to have along the study constant parameters settings of safety window
# length, regardless of the cohort size.
my_win_len <- SafetyWindowConst(
  gap = c(7, 5, 3),
  follow = 7,
  follow_min = 14
)
# This is to have along the study constant parameters settings of safety window
# length, regardless of the cohort size.
my_win_len <- SafetyWindowConst(
  gap = c(7, 5, 3),
  follow = 7,
  follow_min = 14
)

`SafetyWindowSize`

Description

SafetyWindowSize is the class for safety window length based on cohort size. This class is used to decide the rolling rule from the clinical perspective.

Usage

SafetyWindowSize(gap, size, follow, follow_min)

.DefaultSafetyWindowSize()
SafetyWindowSize(gap, size, follow, follow_min)

.DefaultSafetyWindowSize()

Arguments

gap

see slot definition.

size

see slot definition.

follow

see slot definition.

follow_min

see slot definition.

Slots

gap: (list)
observed period of the previous patient before the next patient can be dosed. This is used as follows. If for instance, the cohort size is 4 and we want to specify three time intervals between these four consecutive patients, i.e. 7 units of time between the 1st and the 2nd patient, 5 units between the 2nd and the 3rd one, and finally 3 units between the 3rd and the 4th one, then, gap = list(c(7L, 5L, 3L)). Sometimes, we want that the interval only between the 1st and 2nd patient should be increased for the safety consideration and the rest time intervals should remain constant, regardless of what the cohort size is. Then, gap = list(c(7L, 3L)) and the the package will automatically repeat the last element of the vector for the remaining time intervals.
size: (integer)
a vector with the left bounds of the relevant cohort size intervals. This is used as follows. For instance, when we want to change the gap based on the cohort size, i.e. the time interval between the 1st and 2nd patient = 9 units of time and the rest time intervals are of 5 units of time when the cohort size is equal to or larger than 4. And the time interval between the 1st and 2nd patient = 7 units of time and the rest time intervals are 3 units of time when the cohort size is smaller than 4, then we specify both gap = list(c(7, 3), c(9, 5)) and size = c(0L, 4L). This means, the right bounds of the intervals are excluded from the interval, and the last interval goes from the last value to infinity.
follow: (count)
the period of time that each patient in the cohort needs to be followed before the next cohort opens.
follow_min: (count)
at least one patient in the cohort needs to be followed at the minimal follow up time.

Note

Typically, end users will not use the .DefaultSafetyWindowSize() function.

Examples

# Rule for having patient gap (7,3,3,3,...) for cohort size < 4, and
# patient gap (9,5,5,5...) for cohort size >= 4.
my_window_len <- SafetyWindowSize(
  gap = list(c(7, 3), c(9, 5)),
  size = c(1, 4),
  follow = 7,
  follow_min = 14
)
# Rule for having patient gap (7,3,3,3,...) for cohort size < 4, and
# patient gap (9,5,5,5...) for cohort size >= 4.
my_window_len <- SafetyWindowSize(
  gap = list(c(7, 3), c(9, 5)),
  size = c(1, 4),
  follow = 7,
  follow_min = 14
)

`Samples`

Description

Samples is the class to store the MCMC samples.

Usage

Samples(data, options)

.DefaultSamples()
Samples(data, options)

.DefaultSamples()

Arguments

data

see slot definition.

options

see slot definition.

Slots

data: (list)
MCMC samples of the parameter. Each entry in this list must be a vector (in case of a scalar parameter) or matrix (in case of a vector-valued parameter) with samples. In case of matrix, every row is a separate sample, while columns correspond to the dimension of the parameter.
options: (McmcOptions)
MCMC options that were used to generate the samples.

Note

Typically, end users will not use the .DefaultSamples() function.

Examples

# The MCMC options that were used to generate the samples.
my_options <- McmcOptions(
  burnin = 1000,
  step = 2,
  samples = 1000
)

# Create an object of class 'Samples'
# Here the parameters 'alpha' and 'beta' are randomly generated. Of course, in
# a real example these would be a samples coming from MCMC procedures.
my_samples <- Samples(
  data = list(alpha = rnorm(1000), beta = rnorm(1000)),
  options = my_options
)
# The MCMC options that were used to generate the samples.
my_options <- McmcOptions(
  burnin = 1000,
  step = 2,
  samples = 1000
)

# Create an object of class 'Samples'
# Here the parameters 'alpha' and 'beta' are randomly generated. Of course, in
# a real example these would be a samples coming from MCMC procedures.
my_samples <- Samples(
  data = list(alpha = rnorm(1000), beta = rnorm(1000)),
  options = my_options
)

Determining if this Sample Should be Saved

Description

A method that determines if a sample from a given iteration should be saved. The sample should be saved if and only if: it is not in burn-in period and it matches the step.

Usage

saveSample(object, iteration, ...)

## S4 method for signature 'McmcOptions'
saveSample(object, iteration, ...)
saveSample(object, iteration, ...)

## S4 method for signature 'McmcOptions'
saveSample(object, iteration, ...)

Arguments

object

(McmcOptions)
object based on which the answer is determined.

iteration

(count)
the current iteration index.

...

not used.

Value

TRUE if this sample should be saved.

Functions

saveSample(McmcOptions): determine if a sample should be saved.

Examples

# Set up the MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples.
my_options <- McmcOptions(burnin = 10000, step = 2, samples = 10000)

size(my_options)
saveSample(my_options, iteration = 5)
# Set up the MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples.
my_options <- McmcOptions(burnin = 10000, step = 2, samples = 10000)

size(my_options)
saveSample(my_options, iteration = 5)

Helper Function to Set and Save the RNG Seed

Description

This code is basically copied from stats:::simulate.lm.

Usage

set_seed(seed = NULL)
set_seed(seed = NULL)

Arguments

seed

an object specifying if and how the random number generator should be initialized ("seeded"). Either NULL (default) or an integer that will be used in a call to set.seed() before simulating the response vectors. If set, the value is saved as the seed slot of the returned object. The default, NULL will not change the random generator state.

Value

The integer vector containing the random number generate state will be returned, in order to call this function with this input to reproduce the obtained simulation results.

Show the Summary of Dual-Endpoint Simulations

Description

Display a summary of dual-endpoint simulation results.

Usage

## S4 method for signature 'DualSimulationsSummary'
show(object)
## S4 method for signature 'DualSimulationsSummary'
show(object)

Arguments

object

(DualSimulationsSummary)
the object we want to print.

Value

Invisibly returns a data frame of the results with one row and appropriate column names.

Examples

# Define the dose-grid.
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 30))

# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)

# Stop with a small number of patients for illustration.
my_stopping <- my_stopping1 | StoppingMinPatients(10) | StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
my_design <- DualDesign(
  model = my_model,
  data = emptydata,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = CohortSizeConst(3),
  startingDose = 3
)

# Define scenarios for the TRUE toxicity and efficacy profiles.
beta_mod <- function(dose, e0, eMax, delta1, delta2, scal) {
  maxDens <- (delta1^delta1) *
    (delta2^delta2) /
    ((delta1 + delta2)^(delta1 + delta2))
  dose <- dose / scal
  e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
}

true_biomarker <- function(dose) {
  beta_mod(
    dose,
    e0 = 0.2,
    eMax = 0.6,
    delta1 = 5,
    delta2 = 5 * 0.5 / 0.5,
    scal = 100
  )
}

true_tox <- function(dose) {
  pnorm((dose - 60) / 10)
}

# Draw the TRUE profiles.
par(mfrow = c(1, 2))
curve(true_tox(x), from = 0, to = 80)
curve(true_biomarker(x), from = 0, to = 80)

# Run the simulation on the desired design.
# For illustration purposes only 1 trial outcome is generated and 5 burn-ins
# to generate 20 samples are used here.
my_sims <- simulate(
  object = my_design,
  trueTox = true_tox,
  trueBiomarker = true_biomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 3,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 5,
    step = 1,
    samples = 20
  )
)

# Show the summary of the simulations.
show(summary(
  my_sims,
  trueTox = true_tox,
  trueBiomarker = true_biomarker
))
# Define the dose-grid.
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 30))

# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
my_size2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)

# Stop with a small number of patients for illustration.
my_stopping <- my_stopping1 | StoppingMinPatients(10) | StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
my_design <- DualDesign(
  model = my_model,
  data = emptydata,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = CohortSizeConst(3),
  startingDose = 3
)

# Define scenarios for the TRUE toxicity and efficacy profiles.
beta_mod <- function(dose, e0, eMax, delta1, delta2, scal) {
  maxDens <- (delta1^delta1) *
    (delta2^delta2) /
    ((delta1 + delta2)^(delta1 + delta2))
  dose <- dose / scal
  e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
}

true_biomarker <- function(dose) {
  beta_mod(
    dose,
    e0 = 0.2,
    eMax = 0.6,
    delta1 = 5,
    delta2 = 5 * 0.5 / 0.5,
    scal = 100
  )
}

true_tox <- function(dose) {
  pnorm((dose - 60) / 10)
}

# Draw the TRUE profiles.
par(mfrow = c(1, 2))
curve(true_tox(x), from = 0, to = 80)
curve(true_biomarker(x), from = 0, to = 80)

# Run the simulation on the desired design.
# For illustration purposes only 1 trial outcome is generated and 5 burn-ins
# to generate 20 samples are used here.
my_sims <- simulate(
  object = my_design,
  trueTox = true_tox,
  trueBiomarker = true_biomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 3,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 5,
    step = 1,
    samples = 20
  )
)

# Show the summary of the simulations.
show(summary(
  my_sims,
  trueTox = true_tox,
  trueBiomarker = true_biomarker
))

Show `Simulations` Objects

Description

Display a brief representation of the GeneralSimulations object.

Usage

## S4 method for signature 'GeneralSimulations'
show(object)
## S4 method for signature 'GeneralSimulations'
show(object)

Arguments

object

(GeneralSimulations)
the object we want to print.

Value

Invisibly returns the object itself.

Show the Summary of the Simulations

Description

Display a summary of general simulation results.

Usage

## S4 method for signature 'GeneralSimulationsSummary'
show(object)
## S4 method for signature 'GeneralSimulationsSummary'
show(object)

Arguments

object

(GeneralSimulationsSummary)
the object we want to print.

Value

Invisibly returns a data frame of the results with one row and appropriate column names.

Show the Summary of `PseudoDualSimulations`

Description

Display a summary of pseudo dual simulation results.

Usage

## S4 method for signature 'PseudoDualSimulationsSummary'
show(object)
## S4 method for signature 'PseudoDualSimulationsSummary'
show(object)

Arguments

object

(PseudoDualSimulationsSummary)
the object we want to print.

Value

Invisibly returns a data frame of the results with one row and appropriate column names.

Examples

# Example where DLE and efficacy responses are considered in the simulations.
# In simulations where no samples are used a data object with doses >= 1 needs
# to be generated.
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model of 'ModelEff' (e.g 'Effloglog') class.
eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGain' class.
my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Cohort size of 3.
my_size <- CohortSizeConst(size = 3)

# Stop when 36 subjects are treated or next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design. (For details please refer to the 'DualResponsesDesign' example.)
my_design <- DualResponsesDesign(
  nextBest = my_next_best,
  model = dle_model,
  eff_model = eff_model,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the true DLE and efficacy curves.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)
my_truth_eff <- efficacyFunction(eff_model, theta1 = -4.818429, theta2 = 3.653058)


# For illustration purpose only 2 simulations are produced.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 2,
  seed = 819,
  parallel = FALSE
)

# Summary of the simulations.
my_sum <- summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Show the summary of the simulations in a data frame.
show(my_sum)


# Example when DLE and efficacy samples are involved.

# The escalation rule using the 'NextBestMaxGainSamples' class.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# The design of 'DualResponsesSamplesDesign' class.
my_design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)

# MCMC options.
# For illustration purpose 50 burn-ins to generate 200 samples are used.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)


# For illustration purpose 2 trials are simulated.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 2,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)

# Produce a summary of the simulations.
my_sum <- summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Show the summary in data frame for the simulations.
show(my_sum)

# Example where DLE and efficacy responses are considered in the simulations.
# In simulations where no samples are used a data object with doses >= 1 needs
# to be generated.
emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model of 'ModelEff' (e.g 'Effloglog') class.
eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGain' class.
my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Cohort size of 3.
my_size <- CohortSizeConst(size = 3)

# Stop when 36 subjects are treated or next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design. (For details please refer to the 'DualResponsesDesign' example.)
my_design <- DualResponsesDesign(
  nextBest = my_next_best,
  model = dle_model,
  eff_model = eff_model,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the true DLE and efficacy curves.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)
my_truth_eff <- efficacyFunction(eff_model, theta1 = -4.818429, theta2 = 3.653058)


# For illustration purpose only 2 simulations are produced.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 2,
  seed = 819,
  parallel = FALSE
)

# Summary of the simulations.
my_sum <- summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Show the summary of the simulations in a data frame.
show(my_sum)


# Example when DLE and efficacy samples are involved.

# The escalation rule using the 'NextBestMaxGainSamples' class.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# The design of 'DualResponsesSamplesDesign' class.
my_design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)

# MCMC options.
# For illustration purpose 50 burn-ins to generate 200 samples are used.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)


# For illustration purpose 2 trials are simulated.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 2,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)

# Produce a summary of the simulations.
my_sum <- summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Show the summary in data frame for the simulations.
show(my_sum)

Show the Summary of `PseudoSimulations`

Description

Display a summary of pseudo simulation results.

Usage

## S4 method for signature 'PseudoSimulationsSummary'
show(object)
## S4 method for signature 'PseudoSimulationsSummary'
show(object)

Arguments

object

(PseudoSimulationsSummary)
the object we want to print.

Value

Invisibly returns a data frame of the results with one row and appropriate column names.

Examples

# Obtain the plot for the simulation results if only DLE responses are
# considered in the simulations.

# Specified simulations when no DLE samples are used.
emptydata <- Data(doseGrid = seq(25, 300, 25))

# The design only incorporate DLE responses and DLE samples are involved.
# Specify the model of 'ModelTox' class eg 'LogisticIndepBeta' class model.
my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The escalation rule.
td_next_best <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# The cohort size is 3 subjects.
my_size <- CohortSizeConst(size = 3)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Specify the stopping rule with maximum sample size of 36 patients or when the
# next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design. (For details please refer to the 'TDDesign' example.)
my_design <- TDDesign(
  model = my_model,
  nextBest = td_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the truth of the DLE responses.
my_truth <- probFunction(my_model, phi1 = -53.66584, phi2 = 10.50499)

# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  truth = my_truth,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# Summary of the simulations.
my_sum <- summary(
  my_sim,
  truth = my_truth
)

# Show the summary of the simulated results in a data frame.
show(my_sum)

# Example where DLE samples are involved.

# The escalation rule.
td_next_best <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

# The design.
my_design <- TDsamplesDesign(
  model = my_model,
  nextBest = td_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# For illustration purposes 2 trails are simulated with 50 burn-ins to generate
# 200 samples.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)

my_sim <- simulate(
  object = my_design,
  args = NULL,
  truth = my_truth,
  nsim = 2,
  seed = 819,
  mcmcOptions = my_options,
  parallel = FALSE
)

# Produce a summary of the simulations.
my_sum <- summary(
  my_sim,
  truth = my_truth
)

# Show the summary of the simulated results in a data frame.
show(my_sum)
# Obtain the plot for the simulation results if only DLE responses are
# considered in the simulations.

# Specified simulations when no DLE samples are used.
emptydata <- Data(doseGrid = seq(25, 300, 25))

# The design only incorporate DLE responses and DLE samples are involved.
# Specify the model of 'ModelTox' class eg 'LogisticIndepBeta' class model.
my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The escalation rule.
td_next_best <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# The cohort size is 3 subjects.
my_size <- CohortSizeConst(size = 3)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Specify the stopping rule with maximum sample size of 36 patients or when the
# next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design. (For details please refer to the 'TDDesign' example.)
my_design <- TDDesign(
  model = my_model,
  nextBest = td_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the truth of the DLE responses.
my_truth <- probFunction(my_model, phi1 = -53.66584, phi2 = 10.50499)

# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  truth = my_truth,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# Summary of the simulations.
my_sum <- summary(
  my_sim,
  truth = my_truth
)

# Show the summary of the simulated results in a data frame.
show(my_sum)

# Example where DLE samples are involved.

# The escalation rule.
td_next_best <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

# The design.
my_design <- TDsamplesDesign(
  model = my_model,
  nextBest = td_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# For illustration purposes 2 trails are simulated with 50 burn-ins to generate
# 200 samples.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)

my_sim <- simulate(
  object = my_design,
  args = NULL,
  truth = my_truth,
  nsim = 2,
  seed = 819,
  mcmcOptions = my_options,
  parallel = FALSE
)

# Produce a summary of the simulations.
my_sum <- summary(
  my_sim,
  truth = my_truth
)

# Show the summary of the simulated results in a data frame.
show(my_sum)

Show the Summary of Model-Based Design Simulations

Description

Display a summary of model-based design simulation results.

Usage

## S4 method for signature 'SimulationsSummary'
show(object)
## S4 method for signature 'SimulationsSummary'
show(object)

Arguments

object

(SimulationsSummary)
the object we want to print.

Value

Invisibly returns a data frame of the results with one row and appropriate column names.

Examples

# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3 | StoppingMissingDose()

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design
design <- Design(
  model = model,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = emptydata,
  startingDose = 3
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 1000
)
time <- system.time(
  mySims <- simulate(
    design,
    args = NULL,
    truth = myTruth,
    nsim = 1,
    seed = 819,
    mcmcOptions = options,
    parallel = FALSE
  )
)[3]

# Show the Summary of the Simulations
show(summary(mySims, truth = myTruth))

# nolint end
# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3 | StoppingMissingDose()

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design
design <- Design(
  model = model,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = emptydata,
  startingDose = 3
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 1000
)
time <- system.time(
  mySims <- simulate(
    design,
    args = NULL,
    truth = myTruth,
    nsim = 1,
    seed = 819,
    mcmcOptions = options,
    parallel = FALSE
  )
)[3]

# Show the Summary of the Simulations
show(summary(mySims, truth = myTruth))

# nolint end

Simulate outcomes from a time-to-DLT augmented CRM design

Description

This method simulates dose escalation trials using time-to-DLT data, where the timing of dose-limiting toxicities is explicitly modeled.

Usage

## S4 method for signature 'DADesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truthTox,
  truthSurv,
  trueTmax = NULL,
  args = NULL,
  firstSeparate = FALSE,
  deescalate = TRUE,
  mcmcOptions = McmcOptions(),
  DA = TRUE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  derive = list(),
  ...
)
## S4 method for signature 'DADesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truthTox,
  truthSurv,
  trueTmax = NULL,
  args = NULL,
  firstSeparate = FALSE,
  deescalate = TRUE,
  mcmcOptions = McmcOptions(),
  DA = TRUE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  derive = list(),
  ...
)

Arguments

object

the DADesign object we want to simulate data from

nsim

(count)
the number of simulations (default: 1)

seed

see set_seed()

truthTox

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity and the time DLT occurs. Additional arguments can be supplied in args.

truthSurv

(function)
a CDF which takes as input a time (vector) and returns the true cumulative probability (vector) that the DLT would occur conditioning on the patient has DLTs.

trueTmax

(number or NULL)
the true maximum time at which DLTs can occur. Note that this must be larger than Tmax from the object's base data, which is the length of the DLT window, i.e. until which time DLTs are officially declared as such and used in the trial.

args

(data.frame)
data frame with arguments for the truthTox function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the nsim simulations. In order to produce outcomes from the posterior predictive distribution, e.g, pass an object that contains the data observed so far, truthTox contains the prob function from the model in object, and args contains posterior samples from the model.

firstSeparate

(flag)
enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.

deescalate

(flag)
allow deescalation when a DLT occurs in cohorts with lower dose level? (default: TRUE)

mcmcOptions

(McmcOptions)
object of class McmcOptions, giving the MCMC options for each evaluation in the trial. By default, the standard options are used.

DA

(flag)
use dose-adaptation rules? (default: TRUE)

parallel

(flag)
should the simulation runs be parallelized across the clusters of the computer? (not default)

nCores

(count)
how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.

derive

(list)
a named list of functions which derives statistics, based on the vector of posterior MTD samples. Each list element must therefore accept one and only one argument, which is a numeric vector, and return a number.

...

not used

Value

an object of class Simulations

Note

Backfill cohorts are not yet implemented and therefore will lead to an error if used in the DADesign object.

Examples

# nolint start

# Define the dose-grid and PEM parameters
emptydata <- DataDA(
  doseGrid = c(0.1, 0.5, 1, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  Tmax = 60
)

# Initialize the mDA-CRM model
npiece_ <- 10
Tmax_ <- 60

lambda_prior <- function(k) {
  npiece_ / (Tmax_ * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(as.matrix(c(1:npiece_), 1, npiece_), 2, lambda_prior))),
  c_par = 2
)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping2 <- StoppingMinPatients(nPatients = 50)

myStopping <- (myStopping1 | myStopping2) | StoppingMissingDose()

# Choose the safety window
mysafetywindow <- SafetyWindowConst(c(6, 2), 7, 7)

# Initialize the design
design <- DADesign(
  model = model,
  increments = myIncrements,
  nextBest = myNextBest,
  stopping = myStopping,
  cohort_size = mySize,
  data = emptydata,
  safetyWindow = mysafetywindow,
  startingDose = 3
)

## set up truth curves
myTruth <- probFunction(model, alpha0 = 2, alpha1 = 3)
curve(myTruth(x), from = 0, to = 100, ylim = c(0, 1))

exp_cond.cdf <- function(x, onset = 15) {
  a <- pexp(28, 1 / onset, lower.tail = FALSE)
  1 - (pexp(x, 1 / onset, lower.tail = FALSE) - a) / (1 - a)
}

# set up simulation settings
options <- McmcOptions(
  burnin = 10,
  step = 1,
  samples = 200
)


mySims <- simulate(design,
  args = NULL,
  truthTox = myTruth,
  truthSurv = exp_cond.cdf,
  trueTmax = 80,
  nsim = 2,
  seed = 819,
  mcmcOptions = options,
  firstSeparate = TRUE,
  deescalate = FALSE,
  parallel = FALSE
)


# nolint end
# nolint start

# Define the dose-grid and PEM parameters
emptydata <- DataDA(
  doseGrid = c(0.1, 0.5, 1, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  Tmax = 60
)

# Initialize the mDA-CRM model
npiece_ <- 10
Tmax_ <- 60

lambda_prior <- function(k) {
  npiece_ / (Tmax_ * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(as.matrix(c(1:npiece_), 1, npiece_), 2, lambda_prior))),
  c_par = 2
)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping2 <- StoppingMinPatients(nPatients = 50)

myStopping <- (myStopping1 | myStopping2) | StoppingMissingDose()

# Choose the safety window
mysafetywindow <- SafetyWindowConst(c(6, 2), 7, 7)

# Initialize the design
design <- DADesign(
  model = model,
  increments = myIncrements,
  nextBest = myNextBest,
  stopping = myStopping,
  cohort_size = mySize,
  data = emptydata,
  safetyWindow = mysafetywindow,
  startingDose = 3
)

## set up truth curves
myTruth <- probFunction(model, alpha0 = 2, alpha1 = 3)
curve(myTruth(x), from = 0, to = 100, ylim = c(0, 1))

exp_cond.cdf <- function(x, onset = 15) {
  a <- pexp(28, 1 / onset, lower.tail = FALSE)
  1 - (pexp(x, 1 / onset, lower.tail = FALSE) - a) / (1 - a)
}

# set up simulation settings
options <- McmcOptions(
  burnin = 10,
  step = 1,
  samples = 200
)


mySims <- simulate(design,
  args = NULL,
  truthTox = myTruth,
  truthSurv = exp_cond.cdf,
  trueTmax = 80,
  nsim = 2,
  seed = 819,
  mcmcOptions = options,
  firstSeparate = TRUE,
  deescalate = FALSE,
  parallel = FALSE
)


# nolint end

Simulate outcomes from a CRM design

Description

Usage

## S4 method for signature 'Design'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  truthResponse = plogis,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  derive = list(),
  ...
)
## S4 method for signature 'Design'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  truthResponse = plogis,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  derive = list(),
  ...
)

Arguments

object

the Design object we want to simulate data from

nsim

(count)
the number of simulations (default: 1)

seed

see set_seed()

truth

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity. Additional arguments can be supplied in args.

truthResponse

(function)
a function which takes as input a dose (vector) and returns the probability (vector) for a positive efficacy response.

args

(data.frame)
data frame with arguments for the truth function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the nsim simulations. In order to produce outcomes from the posterior predictive distribution, e.g, pass an object that contains the data observed so far, truth contains the prob function from the model in object, and args contains posterior samples from the model.

firstSeparate

(flag)
enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.

mcmcOptions

(McmcOptions)
object of class McmcOptions, giving the MCMC options for each evaluation in the trial. By default, the standard options are used

parallel

(flag)
should the simulation runs be parallelized across the clusters of the computer? (not default)

nCores

(count)
how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.

derive

...

not used

Value

an object of class Simulations

Examples

# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3 | StoppingMissingDose()

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design
design <- Design(
  model = model,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = emptydata,
  startingDose = 3
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(
  burnin = 5,
  step = 1,
  samples = 10
)

time <- system.time(
  mySims <- simulate(
    design,
    args = NULL,
    truth = myTruth,
    nsim = 1,
    seed = 819,
    mcmcOptions = options,
    parallel = FALSE
  )
)[3]

# nolint end
# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3 | StoppingMissingDose()

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design
design <- Design(
  model = model,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = emptydata,
  startingDose = 3
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(
  burnin = 5,
  step = 1,
  samples = 10
)

time <- system.time(
  mySims <- simulate(
    design,
    args = NULL,
    truth = myTruth,
    nsim = 1,
    seed = 819,
    mcmcOptions = options,
    parallel = FALSE
  )
)[3]

# nolint end

Simulate Method for the `DesignGrouped` Class

Description

A simulate method for DesignGrouped designs.

Usage

## S4 method for signature 'DesignGrouped'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  combo_truth,
  args = data.frame(),
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallelly::availableCores(), 5),
  ...
)
## S4 method for signature 'DesignGrouped'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  combo_truth,
  args = data.frame(),
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallelly::availableCores(), 5),
  ...
)

Arguments

object

(DesignGrouped)
the design we want to simulate trials from.

nsim

(number)
how many trials should be simulated.

seed

(RNGstate)
generated with set_seed().

truth

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity for the mono arm. Additional arguments can be supplied in args.

combo_truth

(function)
same as truth but for the combo arm.

args

(data.frame)
optional data.frame with arguments that work for both the truth and combo_truth functions. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the nsim simulations.

firstSeparate

(flag)
whether to enroll the first patient separately from the rest of the cohort and close the cohort in case a DLT occurs in this first patient.

mcmcOptions

(McmcOptions)
MCMC options for each evaluation in the trial.

parallel

(flag)
whether the simulation runs are parallelized across the cores of the computer.

nCores

(number)
how many cores should be used for parallel computing.

...

not used.

Value

A list of mono and combo simulation results as Simulations objects.

Examples

# Assemble ingredients for our group design.
my_stopping <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5) |
  StoppingMinPatients(10) |
  StoppingMissingDose()
my_increments <- IncrementsDoseLevels(levels = 3L)
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.3),
  overdose = c(0.3, 1),
  max_overdose_prob = 0.3
)
my_cohort_size <- CohortSizeConst(3)
empty_data <- Data(
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
my_model <- LogisticLogNormalGrouped(
  mean = c(-4, -4, -4, -4),
  cov = diag(rep(6, 4)),
  ref_dose = 0.1
)

# Put together the design. Note that if we only specify the mono arm,
# then the combo arm is having the same settings.
my_design <- DesignGrouped(
  model = my_model,
  mono = Design(
    model = my_model,
    stopping = my_stopping,
    increments = my_increments,
    nextBest = my_next_best,
    cohort_size = my_cohort_size,
    data = empty_data,
    startingDose = 0.1
  ),
  first_cohort_mono_only = TRUE,
  same_dose_for_all = TRUE
)

# Set up a realistic simulation scenario.
my_truth <- function(x) plogis(-4 + 0.2 * log(x / 0.1))
my_combo_truth <- function(x) plogis(-4 + 0.5 * log(x / 0.1))
matplot(
  x = empty_data@doseGrid,
  y = cbind(
    mono = my_truth(empty_data@doseGrid),
    combo = my_combo_truth(empty_data@doseGrid)
  ),
  type = "l",
  ylab = "true DLT prob",
  xlab = "dose"
)
legend("topright", c("mono", "combo"), lty = c(1, 2), col = c(1, 2))

# Start the simulations.
set.seed(123)

my_sims <- simulate(
  my_design,
  nsim = 1, # This should be at least 100 in actual applications.
  seed = 123,
  truth = my_truth,
  combo_truth = my_combo_truth
)

# Looking at the summary of the simulations:
mono_sims_sum <- summary(my_sims$mono, truth = my_truth)
combo_sims_sum <- summary(my_sims$combo, truth = my_combo_truth)

mono_sims_sum
combo_sims_sum

plot(mono_sims_sum)
plot(combo_sims_sum)

# Looking at specific simulated trials:
trial_index <- 1
plot(my_sims$mono@data[[trial_index]])
plot(my_sims$combo@data[[trial_index]])

# Assemble ingredients for our group design.
my_stopping <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5) |
  StoppingMinPatients(10) |
  StoppingMissingDose()
my_increments <- IncrementsDoseLevels(levels = 3L)
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.3),
  overdose = c(0.3, 1),
  max_overdose_prob = 0.3
)
my_cohort_size <- CohortSizeConst(3)
empty_data <- Data(
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)
my_model <- LogisticLogNormalGrouped(
  mean = c(-4, -4, -4, -4),
  cov = diag(rep(6, 4)),
  ref_dose = 0.1
)

# Put together the design. Note that if we only specify the mono arm,
# then the combo arm is having the same settings.
my_design <- DesignGrouped(
  model = my_model,
  mono = Design(
    model = my_model,
    stopping = my_stopping,
    increments = my_increments,
    nextBest = my_next_best,
    cohort_size = my_cohort_size,
    data = empty_data,
    startingDose = 0.1
  ),
  first_cohort_mono_only = TRUE,
  same_dose_for_all = TRUE
)

# Set up a realistic simulation scenario.
my_truth <- function(x) plogis(-4 + 0.2 * log(x / 0.1))
my_combo_truth <- function(x) plogis(-4 + 0.5 * log(x / 0.1))
matplot(
  x = empty_data@doseGrid,
  y = cbind(
    mono = my_truth(empty_data@doseGrid),
    combo = my_combo_truth(empty_data@doseGrid)
  ),
  type = "l",
  ylab = "true DLT prob",
  xlab = "dose"
)
legend("topright", c("mono", "combo"), lty = c(1, 2), col = c(1, 2))

# Start the simulations.
set.seed(123)

my_sims <- simulate(
  my_design,
  nsim = 1, # This should be at least 100 in actual applications.
  seed = 123,
  truth = my_truth,
  combo_truth = my_combo_truth
)

# Looking at the summary of the simulations:
mono_sims_sum <- summary(my_sims$mono, truth = my_truth)
combo_sims_sum <- summary(my_sims$combo, truth = my_combo_truth)

mono_sims_sum
combo_sims_sum

plot(mono_sims_sum)
plot(combo_sims_sum)

# Looking at specific simulated trials:
trial_index <- 1
plot(my_sims$mono@data[[trial_index]])
plot(my_sims$combo@data[[trial_index]])

Simulate outcomes from a dual-endpoint design

Description

Usage

## S4 method for signature 'DualDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueTox,
  trueBiomarker,
  args = NULL,
  sigma2W,
  rho = 0,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  derive = list(),
  ...
)
## S4 method for signature 'DualDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueTox,
  trueBiomarker,
  args = NULL,
  sigma2W,
  rho = 0,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  derive = list(),
  ...
)

Arguments

object

the DualDesign object we want to simulate data from

nsim

(count)
the number of simulations (default: 1)

seed

see set_seed()

trueTox

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity. Additional arguments can be supplied in args.

trueBiomarker

(function)
a function which takes as input a dose (vector) and returns the true biomarker level (vector). Additional arguments can be supplied in args.

args

(data.frame)
data frame with arguments for the trueTox and trueBiomarker function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the nsim simulations.

sigma2W

(number)
variance for the biomarker measurements

rho

(number)
correlation between toxicity and biomarker measurements (default: 0)

firstSeparate

(flag)
enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.

mcmcOptions

(McmcOptions)
object of class McmcOptions, giving the MCMC options for each evaluation in the trial. By default, the standard options are used

parallel

(flag)
should the simulation runs be parallelized across the clusters of the computer? (not default)

nCores

(count)
how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.

derive

...

not used

Value

an object of class DualSimulations

Note

Backfill cohorts are not yet implemented and therefore will lead to an error if used in the DualDesign object.

Examples

# nolint start

# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model
model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  use_log_dose = TRUE,
  ref_dose = 2,
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
myStopping <- myStopping4 | StoppingMinPatients(10) | StoppingMissingDose()

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design
design <- DualDesign(
  model = model,
  data = emptydata,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  startingDose = 3
)

# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function(dose, e0, eMax, delta1, delta2, scal) {
  maxDens <- (delta1^delta1) *
    (delta2^delta2) /
    ((delta1 + delta2)^(delta1 + delta2))
  dose <- dose / scal
  e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose) {
  betaMod(
    dose,
    e0 = 0.2,
    eMax = 0.6,
    delta1 = 5,
    delta2 = 5 * 0.5 / 0.5,
    scal = 100
  )
}

trueTox <- function(dose) {
  pnorm((dose - 60) / 10)
}

# Draw the TRUE profiles
par(mfrow = c(1, 2))
curve(trueTox(x), from = 0, to = 80)
curve(trueBiomarker(x), from = 0, to = 80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study
# this should be increased of course, similarly for the McmcOptions -
# they also need to be increased.

mySims <- simulate(
  design,
  trueTox = trueTox,
  trueBiomarker = trueBiomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 3,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 10,
    step = 1,
    samples = 100
  )
)


# nolint end
# nolint start

# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model
model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  use_log_dose = TRUE,
  ref_dose = 2,
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)
myStopping <- myStopping4 | StoppingMinPatients(10) | StoppingMissingDose()

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design
design <- DualDesign(
  model = model,
  data = emptydata,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  startingDose = 3
)

# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function(dose, e0, eMax, delta1, delta2, scal) {
  maxDens <- (delta1^delta1) *
    (delta2^delta2) /
    ((delta1 + delta2)^(delta1 + delta2))
  dose <- dose / scal
  e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose) {
  betaMod(
    dose,
    e0 = 0.2,
    eMax = 0.6,
    delta1 = 5,
    delta2 = 5 * 0.5 / 0.5,
    scal = 100
  )
}

trueTox <- function(dose) {
  pnorm((dose - 60) / 10)
}

# Draw the TRUE profiles
par(mfrow = c(1, 2))
curve(trueTox(x), from = 0, to = 80)
curve(trueBiomarker(x), from = 0, to = 80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study
# this should be increased of course, similarly for the McmcOptions -
# they also need to be increased.

mySims <- simulate(
  design,
  trueTox = trueTox,
  trueBiomarker = trueBiomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 3,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 10,
    step = 1,
    samples = 100
  )
)


# nolint end

Simulate dose escalation procedure using both DLE and efficacy responses without samples

Description

This is a method to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the DualResponsesDesign where DLE model used are of ModelTox class object and efficacy model used are of ModelEff class object. In addition, no DLE and efficacy samples are involved or generated in the simulation process.

Usage

## S4 method for signature 'DualResponsesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueDLE,
  trueEff,
  trueNu,
  args = NULL,
  firstSeparate = FALSE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)
## S4 method for signature 'DualResponsesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueDLE,
  trueEff,
  trueNu,
  args = NULL,
  firstSeparate = FALSE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)

Arguments

object

the DualResponsesDesign object we want to simulate the data from

nsim

(count)
the number of simulations (default: 1)

seed

see set_seed()

trueDLE

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in args.

trueEff

(function)
a function which takes as input a dose (vector) and returns the expected efficacy responses (vector). Additional arguments can be supplied in args.

trueNu

(number)
the precision, the inverse of the variance of the efficacy responses

args

(data.frame)
data frame with arguments for the trueDLE and trueEff function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the nsim simulations.

firstSeparate

(flag)
enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.

parallel

(flag)
should the simulation runs be parallelized across the clusters of the computer? (not default)

nCores

(count)
how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.

...

not used

Value

an object of class PseudoDualSimulations

Examples

# nolint start

## Simulate dose-escalation procedure based on DLE and efficacy responses where no DLE
## and efficacy samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
## First for the DLE model
## The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## The efficacy model of 'ModelEff' (e.g 'Effloglog') class
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)

## The escalation rule using the 'NextBestMaxGain' class
mynextbest <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)


## The increments (see Increments class examples)
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)
## cohort size of 3
mySize <- CohortSizeConst(size = 3)
## Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()
## Now specified the design with all the above information and starting with a dose of 25

## Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(
  nextBest = mynextbest,
  model = DLEmodel,
  eff_model = Effmodel,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)
## Specify the true DLE and efficacy curves
myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499)
myTruthEff <- efficacyFunction(Effmodel, theta1 = -4.818429, theta2 = 3.653058)

## The true gain curve can also be seen
myTruthGain <- function(dose) {
  return((myTruthEff(dose)) / (1 + (myTruthDLE(dose) / (1 - myTruthDLE(dose)))))
}


## Then specified the simulations and generate the trial
## For illustration purpose only 1 simulation is produced (nsim=1).
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
mySim <- simulate(
  object = design,
  args = NULL,
  trueDLE = myTruthDLE,
  trueEff = myTruthEff,
  trueNu = 1 / 0.025,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# nolint end
# nolint start

## Simulate dose-escalation procedure based on DLE and efficacy responses where no DLE
## and efficacy samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
## First for the DLE model
## The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## The efficacy model of 'ModelEff' (e.g 'Effloglog') class
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)

## The escalation rule using the 'NextBestMaxGain' class
mynextbest <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)


## The increments (see Increments class examples)
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)
## cohort size of 3
mySize <- CohortSizeConst(size = 3)
## Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()
## Now specified the design with all the above information and starting with a dose of 25

## Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(
  nextBest = mynextbest,
  model = DLEmodel,
  eff_model = Effmodel,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)
## Specify the true DLE and efficacy curves
myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499)
myTruthEff <- efficacyFunction(Effmodel, theta1 = -4.818429, theta2 = 3.653058)

## The true gain curve can also be seen
myTruthGain <- function(dose) {
  return((myTruthEff(dose)) / (1 + (myTruthDLE(dose) / (1 - myTruthDLE(dose)))))
}


## Then specified the simulations and generate the trial
## For illustration purpose only 1 simulation is produced (nsim=1).
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
mySim <- simulate(
  object = design,
  args = NULL,
  trueDLE = myTruthDLE,
  trueEff = myTruthEff,
  trueNu = 1 / 0.025,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# nolint end

Simulate dose escalation procedure using DLE and efficacy responses with samples

Description

This is a method to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the DualResponsesSamplesDesign where DLE model used are of ModelTox class object and efficacy model used are of ModelEff class object (special case is EffFlexi class model object). In addition, DLE and efficacy samples are involved or generated in the simulation process.

Usage

## S4 method for signature 'DualResponsesSamplesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueDLE,
  trueEff,
  trueNu = NULL,
  trueSigma2 = NULL,
  trueSigma2betaW = NULL,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)
## S4 method for signature 'DualResponsesSamplesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueDLE,
  trueEff,
  trueNu = NULL,
  trueSigma2 = NULL,
  trueSigma2betaW = NULL,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)

Arguments

object

the DualResponsesSamplesDesign object we want to simulate the data from

nsim

(count)
the number of simulations (default: 1)

seed

see set_seed()

trueDLE

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in args.

trueEff

(function)
a function which takes as input a dose (vector) and returns the expected efficacy responses (vector). Additional arguments can be supplied in args.

trueNu

(number)
(not with EffFlexi) the precision, the inverse of the variance of the efficacy responses

trueSigma2

(number)
(only with EffFlexi) the true variance of the efficacy responses which must be a single positive scalar.

trueSigma2betaW

(number)
(only with EffFlexi) the true variance for the random walk model used for smoothing. This must be a single positive scalar.

args

firstSeparate

(flag)
enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.

mcmcOptions

(McmcOptions)
object of class McmcOptions, giving the MCMC options for each evaluation in the trial. By default, the standard options are used

parallel

(flag)
should the simulation runs be parallelized across the clusters of the computer? (not default)

nCores

(count)
how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.

...

not used

Value

an object of class PseudoDualSimulations or PseudoDualFlexiSimulations

Examples

# nolint start

## Simulate dose-escalation procedure based on DLE and efficacy responses where DLE
## and efficacy samples are used
data <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
## First for the DLE model
## The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## The efficacy model of 'ModelEff' (e.g 'Effloglog') class
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)


## The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

## The increments (see Increments class examples)
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)
## cohort size of 3
mySize <- CohortSizeConst(size = 3)
## Stop only when 10 subjects are treated (only for illustration such a low
## sample size)
myStopping <- StoppingMinPatients(nPatients = 10) | StoppingMissingDose()
## Now specified the design with all the above information and starting with
## a dose of 25

## Specified the design
design <- DualResponsesSamplesDesign(
  nextBest = mynextbest,
  cohort_size = mySize,
  startingDose = 25,
  model = DLEmodel,
  eff_model = Effmodel,
  data = data,
  stopping = myStopping,
  increments = myIncrements
)
## specified the true DLE and efficacy curve
myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499)
myTruthEff <- efficacyFunction(Effmodel, theta1 = -4.818429, theta2 = 3.653058)

## The true gain curve can also be seen
myTruthGain <- function(dose) {
  return((myTruthEff(dose)) / (1 + (myTruthDLE(dose) / (1 - myTruthDLE(dose)))))
}

## simulate the trial for 10 times involving samples
## for illustration purposes, in reality larger number of burnin and samples shoud be used
options <- McmcOptions(burnin = 10, step = 1, samples = 50)
## For illustration purpose only 1 simulations are produced (nsim=1).
mySim <- simulate(
  design,
  args = NULL,
  trueDLE = myTruthDLE,
  trueEff = myTruthEff,
  trueNu = 1 / 0.025,
  nsim = 1,
  mcmcOptions = options,
  seed = 819,
  parallel = FALSE
)


## Simulate dose-escalation procedure based on DLE and efficacy responses where DLE
## and efficacy samples are used
## when the efficacy model is of 'EffFlexi' class
Effmodel <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)


## Specified the design
design <- DualResponsesSamplesDesign(
  nextBest = mynextbest,
  cohort_size = mySize,
  startingDose = 25,
  model = DLEmodel,
  eff_model = Effmodel,
  data = data,
  stopping = myStopping,
  increments = myIncrements
)
## specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499)

myTruthEff <- c(
  -0.5478867,
  0.1645417,
  0.5248031,
  0.7604467,
  0.9333009,
  1.0687031,
  1.1793942,
  1.2726408,
  1.3529598,
  1.4233411,
  1.4858613,
  1.5420182
)
## The true gain curve can also be seen
d1 <- data@doseGrid
myTruthGain <- (myTruthEff) / (1 + (myTruthDLE(d1) / (1 - myTruthDLE(d1))))


mySim <- simulate(
  object = design,
  args = NULL,
  trueDLE = myTruthDLE,
  trueEff = myTruthEff,
  trueSigma2 = 0.025,
  trueSigma2betaW = 1,
  mcmcOptions = options,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# nolint end
# nolint start

## Simulate dose-escalation procedure based on DLE and efficacy responses where DLE
## and efficacy samples are used
data <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
## First for the DLE model
## The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## The efficacy model of 'ModelEff' (e.g 'Effloglog') class
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)


## The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

## The increments (see Increments class examples)
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)
## cohort size of 3
mySize <- CohortSizeConst(size = 3)
## Stop only when 10 subjects are treated (only for illustration such a low
## sample size)
myStopping <- StoppingMinPatients(nPatients = 10) | StoppingMissingDose()
## Now specified the design with all the above information and starting with
## a dose of 25

## Specified the design
design <- DualResponsesSamplesDesign(
  nextBest = mynextbest,
  cohort_size = mySize,
  startingDose = 25,
  model = DLEmodel,
  eff_model = Effmodel,
  data = data,
  stopping = myStopping,
  increments = myIncrements
)
## specified the true DLE and efficacy curve
myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499)
myTruthEff <- efficacyFunction(Effmodel, theta1 = -4.818429, theta2 = 3.653058)

## The true gain curve can also be seen
myTruthGain <- function(dose) {
  return((myTruthEff(dose)) / (1 + (myTruthDLE(dose) / (1 - myTruthDLE(dose)))))
}

## simulate the trial for 10 times involving samples
## for illustration purposes, in reality larger number of burnin and samples shoud be used
options <- McmcOptions(burnin = 10, step = 1, samples = 50)
## For illustration purpose only 1 simulations are produced (nsim=1).
mySim <- simulate(
  design,
  args = NULL,
  trueDLE = myTruthDLE,
  trueEff = myTruthEff,
  trueNu = 1 / 0.025,
  nsim = 1,
  mcmcOptions = options,
  seed = 819,
  parallel = FALSE
)


## Simulate dose-escalation procedure based on DLE and efficacy responses where DLE
## and efficacy samples are used
## when the efficacy model is of 'EffFlexi' class
Effmodel <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)


## Specified the design
design <- DualResponsesSamplesDesign(
  nextBest = mynextbest,
  cohort_size = mySize,
  startingDose = 25,
  model = DLEmodel,
  eff_model = Effmodel,
  data = data,
  stopping = myStopping,
  increments = myIncrements
)
## specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499)

myTruthEff <- c(
  -0.5478867,
  0.1645417,
  0.5248031,
  0.7604467,
  0.9333009,
  1.0687031,
  1.1793942,
  1.2726408,
  1.3529598,
  1.4233411,
  1.4858613,
  1.5420182
)
## The true gain curve can also be seen
d1 <- data@doseGrid
myTruthGain <- (myTruthEff) / (1 + (myTruthDLE(d1) / (1 - myTruthDLE(d1))))


mySim <- simulate(
  object = design,
  args = NULL,
  trueDLE = myTruthDLE,
  trueEff = myTruthEff,
  trueSigma2 = 0.025,
  trueSigma2betaW = 1,
  mcmcOptions = options,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# nolint end

Simulate outcomes from a rule-based design

Description

Usage

## S4 method for signature 'RuleDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)
## S4 method for signature 'RuleDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)

Arguments

object

the RuleDesign object we want to simulate data from

nsim

(count)
the number of simulations (default: 1)

seed

see set_seed()

truth

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity. Additional arguments can be supplied in args.

args

parallel

(flag)
should the simulation runs be parallelized across the clusters of the computer? (not default)

nCores

(count)
how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.

...

not used

Value

an object of class GeneralSimulations

Examples

# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(
  nextBest = NextBestThreePlusThree(),
  cohort_size = CohortSizeConst(size = 3L),
  data = emptydata,
  startingDose = 5
)

model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Perform the simulation
## For illustration purpose only 10 simulation is produced (nsim=10).
threeSims <- simulate(
  myDesign,
  nsim = 10,
  seed = 35,
  truth = myTruth,
  parallel = FALSE
)

# nolint end
# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(
  nextBest = NextBestThreePlusThree(),
  cohort_size = CohortSizeConst(size = 3L),
  data = emptydata,
  startingDose = 5
)

model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Perform the simulation
## For illustration purpose only 10 simulation is produced (nsim=10).
threeSims <- simulate(
  myDesign,
  nsim = 10,
  seed = 35,
  truth = myTruth,
  parallel = FALSE
)

# nolint end

Simulate dose escalation procedure using DLE responses only without samples

Description

This is a method to simulate dose escalation procedure only using the DLE responses. This is a method based on the TDDesign where model used are of ModelTox class object and no samples are involved.

Usage

## S4 method for signature 'TDDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)
## S4 method for signature 'TDDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)

Arguments

object

the TDDesign object we want to simulate the data from

nsim

(count)
the number of simulations (default: 1)

seed

see set_seed()

truth

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in args.

args

firstSeparate

(flag)
enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.

parallel

(flag)
should the simulation runs be parallelized across the clusters of the computer? (not default)

nCores

(count)
how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.

...

not used

Value

an object of class PseudoSimulations

Examples

# nolint start

## Simulate dose-escalation procedure based only on DLE responses and no DLE samples are used

## The design comprises a model, the escalation rule, starting data,
## a cohort size and a starting dose
## Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid = seq(25, 300, 25))

## The design only incorporate DLE responses and DLE samples are involved
## Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## Then the escalation rule
tdNextBest <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)
doseRecommendation <- nextBest(
  tdNextBest,
  doselimit = max(data@doseGrid),
  model = model,
  data = data
)
## Then the starting data, an empty data set
emptydata <- Data(doseGrid = seq(25, 300, 25))
## The cohort size, size of 3 subjects
mySize <- CohortSizeConst(size = 3)
## Deifne the increments for the dose-escalation process
## The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
## The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
## This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements <- IncrementsRelative(
  intervals = c(min(data@doseGrid), max(data@doseGrid)),
  increments = c(2, 2)
)
## Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()


## Specified the design(for details please refer to the 'TDDesign' example)
design <- TDDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)

## Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

## then plot the truth to see how the truth dose-DLE curve look like
curve(myTruth(x), from = 0, to = 300, ylim = c(0, 1))


## For illustration purpose only 1 simulation is produced (nsim=1).
mySim <- simulate(
  object = design,
  args = NULL,
  truth = myTruth,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# nolint end
# nolint start

## Simulate dose-escalation procedure based only on DLE responses and no DLE samples are used

## The design comprises a model, the escalation rule, starting data,
## a cohort size and a starting dose
## Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid = seq(25, 300, 25))

## The design only incorporate DLE responses and DLE samples are involved
## Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## Then the escalation rule
tdNextBest <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)
doseRecommendation <- nextBest(
  tdNextBest,
  doselimit = max(data@doseGrid),
  model = model,
  data = data
)
## Then the starting data, an empty data set
emptydata <- Data(doseGrid = seq(25, 300, 25))
## The cohort size, size of 3 subjects
mySize <- CohortSizeConst(size = 3)
## Deifne the increments for the dose-escalation process
## The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
## The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
## This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements <- IncrementsRelative(
  intervals = c(min(data@doseGrid), max(data@doseGrid)),
  increments = c(2, 2)
)
## Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()


## Specified the design(for details please refer to the 'TDDesign' example)
design <- TDDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)

## Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

## then plot the truth to see how the truth dose-DLE curve look like
curve(myTruth(x), from = 0, to = 300, ylim = c(0, 1))


## For illustration purpose only 1 simulation is produced (nsim=1).
mySim <- simulate(
  object = design,
  args = NULL,
  truth = myTruth,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# nolint end

Simulate dose escalation procedure using DLE responses only with DLE samples

Description

This is a method to simulate dose escalation procedure only using the DLE responses. This is a method based on the TDsamplesDesign where model used are of ModelTox class object DLE samples are also used.

Usage

## S4 method for signature 'TDsamplesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)
## S4 method for signature 'TDsamplesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)

Arguments

object

the TDsamplesDesign object we want to simulate the data from

nsim

(count)
the number of simulations (default: 1)

seed

see set_seed()

truth

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in args.

args

firstSeparate

(flag)
enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.

mcmcOptions

(McmcOptions)
object of class McmcOptions, giving the MCMC options for each evaluation in the trial. By default, the standard options are used

parallel

(flag)
should the simulation runs be parallelized across the clusters of the computer? (not default)

nCores

(count)
how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.

...

not used

Value

an object of class PseudoSimulations

Examples

# nolint start

## Simulate dose-escalation procedure based only on DLE responses with DLE samples involved

## The design comprises a model, the escalation rule, starting data,
## a cohort size and a starting dose
## Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid = seq(25, 300, 25))

## The design only incorporate DLE responses and DLE samples are involved
## Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## Then the escalation rule
tdNextBest <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

## The cohort size, size of 3 subjects
mySize <- CohortSizeConst(size = 3)
## Deifne the increments for the dose-escalation process
## The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
## The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
## This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements <- IncrementsRelative(
  intervals = c(min(data@doseGrid), max(data@doseGrid)),
  increments = c(2, 2)
)
## Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

## Specified the design(for details please refer to the 'TDsamplesDesign' example)
design <- TDsamplesDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)

## Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

## then plot the truth to see how the truth dose-DLE curve look like
curve(myTruth(x), from = 0, to = 300, ylim = c(0, 1))

## Then specified the simulations and generate the trial
## options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## The simulations
## For illustration purpose only 1 simulation is produced (nsim=1).
mySim <- simulate(
  object = design,
  args = NULL,
  truth = myTruth,
  nsim = 1,
  seed = 819,
  mcmcOptions = options,
  parallel = FALSE
)

# nolint end
# nolint start

## Simulate dose-escalation procedure based only on DLE responses with DLE samples involved

## The design comprises a model, the escalation rule, starting data,
## a cohort size and a starting dose
## Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid = seq(25, 300, 25))

## The design only incorporate DLE responses and DLE samples are involved
## Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## Then the escalation rule
tdNextBest <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

## The cohort size, size of 3 subjects
mySize <- CohortSizeConst(size = 3)
## Deifne the increments for the dose-escalation process
## The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
## The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
## This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements <- IncrementsRelative(
  intervals = c(min(data@doseGrid), max(data@doseGrid)),
  increments = c(2, 2)
)
## Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

## Specified the design(for details please refer to the 'TDsamplesDesign' example)
design <- TDsamplesDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data,
  startingDose = 25
)

## Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

## then plot the truth to see how the truth dose-DLE curve look like
curve(myTruth(x), from = 0, to = 300, ylim = c(0, 1))

## Then specified the simulations and generate the trial
## options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## The simulations
## For illustration purpose only 1 simulation is produced (nsim=1).
mySim <- simulate(
  object = design,
  args = NULL,
  truth = myTruth,
  nsim = 1,
  seed = 819,
  mcmcOptions = options,
  parallel = FALSE
)

# nolint end

`Simulations`

Description

This class captures the trial simulations from model based designs. Additional slots fit, stop_reasons, stop_report,additional_stats compared to the general class GeneralSimulations.

Usage

Simulations(fit, stop_reasons, stop_report, additional_stats, ...)

.DefaultSimulations()
Simulations(fit, stop_reasons, stop_report, additional_stats, ...)

.DefaultSimulations()

Arguments

fit

(list)
see slot definition.

stop_reasons

(list)
see slot definition.

stop_report

see Simulations

additional_stats

(list)
see slot definition.

...

additional parameters from GeneralSimulations

Slots

fit: (list)
final fits
stop_reasons: (list)
stopping reasons for each simulation run
stop_report: matrix of stopping rule outcomes
additional_stats: list of additional statistical summary

Note

Typically, end users will not use the .DefaultSimulations() function.

Examples

data <- list(
  Data(
    x = 1:2,
    y = 0:1,
    doseGrid = 1:2,
    ID = 1L:2L,
    cohort = 1L:2L
  ),
  Data(
    x = 3:4,
    y = 0:1,
    doseGrid = 3:4,
    ID = 1L:2L,
    cohort = 1L:2L
  )
)

doses <- c(1, 2)

seed <- as.integer(123)

fit <- list(
  c(0.1, 0.2),
  c(0.3, 0.4)
)

stop_report <- matrix(c(TRUE, FALSE), nrow = 2)

stop_reasons <- list("A", "B")

additional_stats <- list(a = 1, b = 1)

simulations <- Simulations(
  fit = fit,
  stop_report = stop_report,
  stop_reasons = stop_reasons,
  additional_stats = additional_stats,
  data,
  doses,
  seed
)
data <- list(
  Data(
    x = 1:2,
    y = 0:1,
    doseGrid = 1:2,
    ID = 1L:2L,
    cohort = 1L:2L
  ),
  Data(
    x = 3:4,
    y = 0:1,
    doseGrid = 3:4,
    ID = 1L:2L,
    cohort = 1L:2L
  )
)

doses <- c(1, 2)

seed <- as.integer(123)

fit <- list(
  c(0.1, 0.2),
  c(0.3, 0.4)
)

stop_report <- matrix(c(TRUE, FALSE), nrow = 2)

stop_reasons <- list("A", "B")

additional_stats <- list(a = 1, b = 1)

simulations <- Simulations(
  fit = fit,
  stop_report = stop_report,
  stop_reasons = stop_reasons,
  additional_stats = additional_stats,
  data,
  doses,
  seed
)

`SimulationsSummary`

Description

In addition to the slots in the parent class GeneralSimulationsSummary, it contains two slots with model fit information.

Usage

.DefaultSimulationsSummary()
.DefaultSimulationsSummary()

Slots

stop_report: (matrix)
matrix of stopping rule outcomes
fit_at_dose_most_selected: (numeric)
fitted toxicity rate at dose most often selected
additional_stats: (list)
list of additional statistical summary
mean_fit: (list)
list with the average, lower (2.5%) and upper (97.5%) quantiles of the mean fitted toxicity at each dose level

Note

Typically, end users will not use the .DefaultSimulationsSummary() function.

Size of an Object

Description

A method that computes the size of a given object. This can be for instance a size of a MCMC sample, or the size of a cohort. See the help of a specific method for more details.

Usage

size(object, ...)

## S4 method for signature 'McmcOptions'
size(object, ...)

## S4 method for signature 'CohortSizeRange'
size(object, dose, data)

## S4 method for signature 'CohortSizeDLT'
size(object, dose, data)

## S4 method for signature 'CohortSizeMax'
size(object, dose, data)

## S4 method for signature 'CohortSizeMin'
size(object, dose, data)

## S4 method for signature 'CohortSizeConst'
size(object, dose, ...)

## S4 method for signature 'CohortSizeRandom'
size(object, dose, ...)

## S4 method for signature 'CohortSizeParts'
size(object, dose, data)

## S4 method for signature 'CohortSizeOrdinal'
size(object, dose, data, ...)

## S4 method for signature 'Samples'
size(object, ...)
size(object, ...)

## S4 method for signature 'McmcOptions'
size(object, ...)

## S4 method for signature 'CohortSizeRange'
size(object, dose, data)

## S4 method for signature 'CohortSizeDLT'
size(object, dose, data)

## S4 method for signature 'CohortSizeMax'
size(object, dose, data)

## S4 method for signature 'CohortSizeMin'
size(object, dose, data)

## S4 method for signature 'CohortSizeConst'
size(object, dose, ...)

## S4 method for signature 'CohortSizeRandom'
size(object, dose, ...)

## S4 method for signature 'CohortSizeParts'
size(object, dose, data)

## S4 method for signature 'CohortSizeOrdinal'
size(object, dose, data, ...)

## S4 method for signature 'Samples'
size(object, ...)

Arguments

object

(McmcOptions or Samples or CohortSize)
an object for which the size is computed.

...

not used.

dose

(numeric) the next dose.

data

the data input, an object of class DataOrdinal.

Value

A size of a given object.

Functions

size(McmcOptions): compute the number of MCMC samples based on McmcOptions object.
size(CohortSizeRange): Determines the size of the next cohort based on the range into which the next dose falls into.
size(CohortSizeDLT): Determines the size of the next cohort based on the number of DLTs so far.
size(CohortSizeMax): Determines the size of the next cohort based on maximum of multiple cohort size rules.
size(CohortSizeMin): Determines the size of the next cohort based on minimum of multiple cohort size rules.
size(CohortSizeConst): Constant cohort size.
size(CohortSizeRandom): Random cohort size drawn uniformly between min and max size.
size(CohortSizeParts): Determines the size of the next cohort based on the parts.
size(CohortSizeOrdinal): Determines the size of the next cohort in a ordinal CRM trial.
size(Samples): get the number of MCMC samples from Samples object.

Examples

# Set up the MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples.
my_options <- McmcOptions(burnin = 10000, step = 2, samples = 10000)

size(my_options)
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for the cohort size:
#   - having cohort of size 1 for doses <10
#   - and having cohort of size 3 for doses >=10
mySize <- CohortSizeRange(intervals = c(0, 10), cohort_size = c(1, 3))

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for the cohort size:
#   - having cohort of size 1 if no DLTs were yet observed
#   - and having cohort of size 3 if at least 1 DLT was already observed
mySize <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(
  intervals = c(0, 10),
  cohort_size = c(1, 3)
)

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

# Combining the two rules for cohort size by taking the maximum of the sample sizes
# of the single rules
mySize <- maxSize(mySize1, mySize2)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

# Combining the two rules for cohort size by taking the minimum of the sample sizes
# of the single rules
mySize <- minSize(mySize1, mySize2)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for having cohorts with constant cohort size of 3
mySize <- CohortSizeConst(size = 3)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value)

# nolint end
# Rule for having cohorts with random cohort size between 2 and 4
mySize <- CohortSizeRandom(min_size = 2, max_size = 4)

# Determine the cohort size for the next cohort
# This will return a random integer between 2 and 4 (inclusive)
set.seed(123)
size(mySize, dose = 5)
# nolint start

# create an object of class 'DataParts'
data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

myIncrements <- IncrementsRelativeParts(
  dlt_start = 0,
  clean_start = 1
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for the cohort size:
mySize <- CohortSizeParts(cohort_sizes = c(1, 3))

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
CohortSizeOrdinal(
  grade = 1L,
  rule = CohortSizeRange(intervals = c(0L, 30L), cohort_size = c(1L, 3L))
)
# Set up the MCMC option in order to have a burn-in of 100 iterations and
# then take every other iteration up to a collection of 200 samples.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 200)

my_samples <- Samples(
  data = list(alpha = rnorm(200), beta = rnorm(200)),
  options = my_options
)

size(my_samples)
# Set up the MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples.
my_options <- McmcOptions(burnin = 10000, step = 2, samples = 10000)

size(my_options)
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for the cohort size:
#   - having cohort of size 1 for doses <10
#   - and having cohort of size 3 for doses >=10
mySize <- CohortSizeRange(intervals = c(0, 10), cohort_size = c(1, 3))

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for the cohort size:
#   - having cohort of size 1 if no DLTs were yet observed
#   - and having cohort of size 3 if at least 1 DLT was already observed
mySize <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(
  intervals = c(0, 10),
  cohort_size = c(1, 3)
)

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

# Combining the two rules for cohort size by taking the maximum of the sample sizes
# of the single rules
mySize <- maxSize(mySize1, mySize2)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  )
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)

# Combining the two rules for cohort size by taking the minimum of the sample sizes
# of the single rules
mySize <- minSize(mySize1, mySize2)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for having cohorts with constant cohort size of 3
mySize <- CohortSizeConst(size = 3)

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value)

# nolint end
# Rule for having cohorts with random cohort size between 2 and 4
mySize <- CohortSizeRandom(min_size = 2, max_size = 4)

# Determine the cohort size for the next cohort
# This will return a random integer between 2 and 4 (inclusive)
set.seed(123)
size(mySize, dose = 5)
# nolint start

# create an object of class 'DataParts'
data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 2000
)
set.seed(94)
samples <- mcmc(data, model, options)

myIncrements <- IncrementsRelativeParts(
  dlt_start = 0,
  clean_start = 1
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Rule for the cohort size:
mySize <- CohortSizeParts(cohort_sizes = c(1, 3))

# Determine the cohort size for the next cohort
size(mySize, dose = doseRecommendation$value, data = data)

# nolint end
CohortSizeOrdinal(
  grade = 1L,
  rule = CohortSizeRange(intervals = c(0L, 30L), cohort_size = c(1L, 3L))
)
# Set up the MCMC option in order to have a burn-in of 100 iterations and
# then take every other iteration up to a collection of 200 samples.
my_options <- McmcOptions(burnin = 100, step = 2, samples = 200)

my_samples <- Samples(
  data = list(alpha = rnorm(200), beta = rnorm(200)),
  options = my_options
)

size(my_samples)

`Stopping`

Description

Stopping is a class for stopping rules.

Slots

report_label: (string)
a label for the stopping report. The meaning of this parameter is twofold. If it is equal to NA_character_ (default), the report_label will not be used in the report at all. Otherwise, if it is specified as an empty character (i.e. character(0)) in a user constructor, then a default, class-specific label will be created for this slot. Finally, for the remaining cases, a user can provide a custom label.

`StoppingAll`

Description

StoppingAll is the class for testing a stopping rule that consists of many single stopping rules that are in turn the objects of class Stopping. All single stopping rules must be satisfied in order the result of this rule to be TRUE.

Usage

StoppingAll(stop_list, report_label = NA_character_)

.DefaultStoppingAll()
StoppingAll(stop_list, report_label = NA_character_)

.DefaultStoppingAll()

Arguments

stop_list

(list)
see slot definition.

report_label

(string)
see slot definition.

Slots

stop_list: (list)
list of stopping rules.
report_label: label for reporting

Note

Typically, end users will not use the .DefaultStoppingAll() function.

Examples

# Define some stopping rules.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
my_stopping3 <- StoppingMinPatients(nPatients = 20)

# Create a list of stopping rules (of class `StoppingAll`) which would then be
# summarized by the `all` function, meaning that the study would be stopped only
# if all of the single stopping rules are `TRUE`.
my_stopping <- StoppingAll(
  stop_list = c(my_stopping1, my_stopping2, my_stopping3)
)
# Define some stopping rules.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
my_stopping3 <- StoppingMinPatients(nPatients = 20)

# Create a list of stopping rules (of class `StoppingAll`) which would then be
# summarized by the `all` function, meaning that the study would be stopped only
# if all of the single stopping rules are `TRUE`.
my_stopping <- StoppingAll(
  stop_list = c(my_stopping1, my_stopping2, my_stopping3)
)

`StoppingAny`

Description

StoppingAny is the class for testing a stopping rule that consists of many single stopping rules that are in turn the objects of class Stopping. At least one single stopping rule must be satisfied in order the result of this rule to be TRUE.

Usage

StoppingAny(stop_list, report_label = NA_character_)

.DefaultStoppingAny()
StoppingAny(stop_list, report_label = NA_character_)

.DefaultStoppingAny()

Arguments

stop_list

(list)
see slot definition.

report_label

(string)
see slot definition.

Slots

stop_list: (list)
list of stopping rules.
report_label: label for reporting

Note

Typically, end users will not use the .DefaultStoppingAny() function.

Examples

# Define some stopping rules.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
my_stopping3 <- StoppingMinPatients(nPatients = 20)

# Create a list of stopping rules (of class `StoppingAny`) which would then be
# summarized by the `any` function, meaning that the study would be stopped if
# any of the single stopping rules is `TRUE`.
my_stopping <- StoppingAny(
  stop_list = c(my_stopping1, my_stopping2, my_stopping3)
)
# Define some stopping rules.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
my_stopping3 <- StoppingMinPatients(nPatients = 20)

# Create a list of stopping rules (of class `StoppingAny`) which would then be
# summarized by the `any` function, meaning that the study would be stopped if
# any of the single stopping rules is `TRUE`.
my_stopping <- StoppingAny(
  stop_list = c(my_stopping1, my_stopping2, my_stopping3)
)

`StoppingCohortsNearDose`

Description

StoppingCohortsNearDose is the class for stopping based on number of cohorts near to next best dose.

Usage

StoppingCohortsNearDose(
  nCohorts = 2L,
  percentage = 50,
  report_label = NA_character_
)

.DefaultStoppingCohortsNearDose()
StoppingCohortsNearDose(
  nCohorts = 2L,
  percentage = 50,
  report_label = NA_character_
)

.DefaultStoppingCohortsNearDose()

Arguments

nCohorts

(number)
see slot definition.

percentage

(number)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

nCohorts: (number)
number of required cohorts.
percentage: (number)
percentage (between and including 0 and 100) within the next best dose the cohorts must lie.

Note

Typically, end users will not use the .DefaultStoppingCohortsNearDose() function.

Examples

# Here, is the rule for stopping the study if at least 3 cohorts were dosed
# at a dose within (1 +/- 0.2) of the next best dose.
my_stopping <- StoppingCohortsNearDose(
  nCohorts = 3,
  percentage = 0.2
)
# Here, is the rule for stopping the study if at least 3 cohorts were dosed
# at a dose within (1 +/- 0.2) of the next best dose.
my_stopping <- StoppingCohortsNearDose(
  nCohorts = 3,
  percentage = 0.2
)

`StoppingExternal`

Description

StoppingExternal is the class for stopping based on an external flag.

Usage

StoppingExternal(report_label = NA_character_)

.DefaultStoppingExternal(report_label = NA_character_)
StoppingExternal(report_label = NA_character_)

.DefaultStoppingExternal(report_label = NA_character_)

Arguments

report_label

(string or NA)
see slot definition.

Note

Typically, end users will not use the .DefaultStoppingExternal() function.

Examples

my_stopping <- StoppingExternal()
my_stopping <- StoppingExternal()

`StoppingHighestDose`

Description

StoppingHighestDose is the class for stopping based on the highest dose. That is, the stopping occurs when the highest dose is reached.

Usage

StoppingHighestDose(report_label = NA_character_)

.DefaultStoppingHighestDose()
StoppingHighestDose(report_label = NA_character_)

.DefaultStoppingHighestDose()

Arguments

report_label

(string or NA)
see slot definition.

Note

Typically, end users will not use the .DefaultStoppingHighestDose() function.

Examples

# The following stopping rule is met when:
# - next proposed dose is highest dose, and
# - there are already at least 3 patients on that dose, and
# - we are sure that this dose is safe, e.g. the probability to be in (0%, 20%)
# interval of the DLT rate is above 50%.
my_stopping <- StoppingHighestDose() &
  StoppingPatientsNearDose(nPatients = 3, percentage = 0) &
  StoppingTargetProb(target = c(0, 0.2), prob = 0.5)

# We note that this rule would then need to be combined with the other standard
# stopping rules, when the MTD is found based on being near e.g. a 30% DLT
# probability or having reached maximal sample size, in the manner of:
# stop_rule <- stop_high | stop_low | stop_sample_size # nolintr.
# The following stopping rule is met when:
# - next proposed dose is highest dose, and
# - there are already at least 3 patients on that dose, and
# - we are sure that this dose is safe, e.g. the probability to be in (0%, 20%)
# interval of the DLT rate is above 50%.
my_stopping <- StoppingHighestDose() &
  StoppingPatientsNearDose(nPatients = 3, percentage = 0) &
  StoppingTargetProb(target = c(0, 0.2), prob = 0.5)

# We note that this rule would then need to be combined with the other standard
# stopping rules, when the MTD is found based on being near e.g. a 30% DLT
# probability or having reached maximal sample size, in the manner of:
# stop_rule <- stop_high | stop_low | stop_sample_size # nolintr.

`StoppingList`

Description

StoppingList is the class for testing a stopping rule that consists of many single stopping rules that are in turn the objects of class Stopping. The summary slot stores a function that takes a logical vector of the size of stop_list and returns a single logical value. For example, if the function all is specified as a summary function, then that all stopping rules defined in stop_list must be satisfied in order the result of this rule to be TRUE.

Usage

StoppingList(stop_list, summary)

.DefaultStoppingList()
StoppingList(stop_list, summary)

.DefaultStoppingList()

Arguments

stop_list

(list)
see slot definition.

summary

(function)
see slot definition.

Slots

stop_list: (list)
list of stopping rules.
summary: (function)
a summary function to combine the results of the stopping rules into a single result.

Note

Typically, end users will not use the .DefaultStoppingList() function.

Examples

# Define some stopping rules.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
my_stopping3 <- StoppingMinPatients(nPatients = 20)

# Create a list of stopping rules (of class `StoppingList`) which will then be
# summarized (in this specific example) with the `any` function, meaning that
# the study would be stopped if any of the single stopping rules is `TRUE`.
my_stopping <- StoppingList(
  stop_list = c(my_stopping1, my_stopping2, my_stopping3),
  summary = any
)
# Define some stopping rules.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
my_stopping3 <- StoppingMinPatients(nPatients = 20)

# Create a list of stopping rules (of class `StoppingList`) which will then be
# summarized (in this specific example) with the `any` function, meaning that
# the study would be stopped if any of the single stopping rules is `TRUE`.
my_stopping <- StoppingList(
  stop_list = c(my_stopping1, my_stopping2, my_stopping3),
  summary = any
)

`StoppingLowestDoseHSRBeta`

Description

StoppingLowestDoseHSRBeta is a class for stopping based on a Hard Safety Rule using the Beta posterior distribution with Beta(a,b) prior and a Bin-Beta model based on the observed data at the lowest dose level. The rule is triggered when the first dose is considered to be toxic (i.e. above threshold probability) based on the observed data at the lowest dose level and a Beta(a,b) prior distribution. The default prior is Beta(1,1). In case that placebo is used, the rule is evaluated at the second dose of the dose grid, i.e. at the lowest non-placebo dose.

Usage

StoppingLowestDoseHSRBeta(
  target = 0.3,
  prob = 0.95,
  a = 1,
  b = 1,
  report_label = NA_character_
)

.DefaultStoppingLowestDoseHSRBeta()
StoppingLowestDoseHSRBeta(
  target = 0.3,
  prob = 0.95,
  a = 1,
  b = 1,
  report_label = NA_character_
)

.DefaultStoppingLowestDoseHSRBeta()

Arguments

target

(proportion)
see slot definition.

prob

(proportion)
see slot definition.

a

(number)
see slot definition.

b

(number)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

target: (proportion)
the target toxicity.
prob: (proportion)
the threshold probability for the lowest dose being toxic.
a: (number)
shape parameter $a > 0$ of probability distribution Beta (a,b).
b: (number)
shape parameter $b > 0$ of probability distribution Beta (a,b).

Note

This stopping rule is independent from the underlying model.

Typically, end users will not use the .DefaultStoppingLowestDoseHSRBeta() function.

Examples

# Stopping the study if the first dose is toxic with more than 90%
# probability based on a Beta posterior distribution with Beta(1,1) prior.
my_stopping <- StoppingLowestDoseHSRBeta(
  target = 0.3,
  prob = 0.9
)

# Stopping the study if the first dose is toxic with more than 90%
# probability based on a Beta posterior distribution with Beta(0.5,0.5) prior.
my_stopping <- StoppingLowestDoseHSRBeta(
  target = 0.3,
  prob = 0.9,
  a = 0.5,
  b = 0.5
)
# Stopping the study if the first dose is toxic with more than 90%
# probability based on a Beta posterior distribution with Beta(1,1) prior.
my_stopping <- StoppingLowestDoseHSRBeta(
  target = 0.3,
  prob = 0.9
)

# Stopping the study if the first dose is toxic with more than 90%
# probability based on a Beta posterior distribution with Beta(0.5,0.5) prior.
my_stopping <- StoppingLowestDoseHSRBeta(
  target = 0.3,
  prob = 0.9,
  a = 0.5,
  b = 0.5
)

`StoppingMaxGainCIRatio`

Description

StoppingMaxGainCIRatio is the class for testing a stopping rule that is based on a target ratio of the 95% credibility interval. Specifically, this is the ratio of the upper to the lower bound of the 95% credibility interval's estimate of the: (1) target dose (i.e. a dose that corresponds to a given target probability of the occurrence of a DLT prob_target), or (2) max gain dose (i.e. a dose which gives the maximum gain), depending on which one out of these two is smaller.

Usage

StoppingMaxGainCIRatio(
  target_ratio = 5,
  prob_target = 0.3,
  report_label = NA_character_
)

.DefaultStoppingMaxGainCIRatio()
StoppingMaxGainCIRatio(
  target_ratio = 5,
  prob_target = 0.3,
  report_label = NA_character_
)

.DefaultStoppingMaxGainCIRatio()

Arguments

target_ratio

(numeric)
see slot definition.

prob_target

(proportion)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

target_ratio: (numeric)
target for the ratio of the 95% credibility interval's estimate, that is required to stop a trial.
prob_target: (proportion)
the target probability of the occurrence of a DLT.

Examples

# Define the target stopping ratio (5) and the target probability of a DLT to
# be used (0.3).
my_stopping <- StoppingMaxGainCIRatio(target_ratio = 5, prob_target = 0.3)
.DefaultStoppingMaxGainCIRatio()
# Define the target stopping ratio (5) and the target probability of a DLT to
# be used (0.3).
my_stopping <- StoppingMaxGainCIRatio(target_ratio = 5, prob_target = 0.3)
.DefaultStoppingMaxGainCIRatio()

`StoppingMinCohorts`

Description

StoppingMinCohorts is the class for stopping based on minimum number of cohorts.

Usage

StoppingMinCohorts(nCohorts = 2L, report_label = NA_character_)

.DefaultStoppingMinCohorts()
StoppingMinCohorts(nCohorts = 2L, report_label = NA_character_)

.DefaultStoppingMinCohorts()

Arguments

nCohorts

(number)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

nCohorts: (number)
minimum required number of cohorts.

Note

Typically, end users will not use the .DefaultStoppingMinCohorts() function.

Examples

# As example, here is the rule for stopping the study if at least 6 cohorts
# were already dosed.
my_stopping <- StoppingMinCohorts(nCohorts = 6)
# As example, here is the rule for stopping the study if at least 6 cohorts
# were already dosed.
my_stopping <- StoppingMinCohorts(nCohorts = 6)

`StoppingMinPatients`

Description

StoppingMinPatients is the class for stopping based on minimum number of patients

Usage

StoppingMinPatients(nPatients = 20L, report_label = NA_character_)

.DefaultStoppingMinPatients()
StoppingMinPatients(nPatients = 20L, report_label = NA_character_)

.DefaultStoppingMinPatients()

Arguments

nPatients

(number)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

nPatients: (number)
minimum allowed number of patients.

Note

Typically, end users will not use the .DefaultStoppingMinPatients() function.

Examples

# As example, here is the rule for stopping the study if at least 20 patients
# were already dosed
my_stopping <- StoppingMinPatients(nPatients = 20)
# As example, here is the rule for stopping the study if at least 20 patients
# were already dosed
my_stopping <- StoppingMinPatients(nPatients = 20)

`StoppingMissingDose`

Description

StoppingMissingDose is the class for stopping based on NA returned by next best dose.

Usage

StoppingMissingDose(report_label = NA_character_)

.DefaultStoppingMissingDose()
StoppingMissingDose(report_label = NA_character_)

.DefaultStoppingMissingDose()

Arguments

report_label

(string or NA)
see slot definition.

Note

Typically, end users will not use the .DefaultStoppingMissingDose() function.

Examples

# The rule for stopping the study if NA or Placebo is returned as
# next best dose.
my_stopping <- StoppingMissingDose()
# The rule for stopping the study if NA or Placebo is returned as
# next best dose.
my_stopping <- StoppingMissingDose()

`StoppingMTDCV`

Description

StoppingMTDCV is a class for stopping rule based on precision of MTD which is calculated as the coefficient of variation (CV) of the MTD. Here, the MTD is defined as the dose that reaches a specific target probability of the occurrence of a DLT.

Usage

StoppingMTDCV(target = 0.3, thresh_cv = 40, report_label = NA_character_)

.DefaultStoppingMTDCV()
StoppingMTDCV(target = 0.3, thresh_cv = 40, report_label = NA_character_)

.DefaultStoppingMTDCV()

Arguments

target

(proportion)
see slot definition.

thresh_cv

(number)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

target: (proportion)
toxicity target of MTD (except 0 or 1).
thresh_cv: (number)
threshold (percentage > 0) for CV to be considered accurate enough to stop the trial. The stopping occurs when the CV is less than or equal to tresh_cv.

Note

Typically, end users will not use the .DefaultStoppingMTDCV() function.

Examples

# Stopping the study if the MTD estimation is precise enough, i.e. if robust
# coefficient of variation of the MTD is below 40%.
my_stopping <- StoppingMTDCV(target = 0.3, thresh_cv = 40)
# Stopping the study if the MTD estimation is precise enough, i.e. if robust
# coefficient of variation of the MTD is below 40%.
my_stopping <- StoppingMTDCV(target = 0.3, thresh_cv = 40)

`StoppingMTDdistribution`

Description

StoppingMTDdistribution is the class for stopping based on the posterior distribution of the MTD. It is used for the cases where the stopping occurs when the probability of MTD > thresh * next_dose is greater than or equal to prob, where the next_dose is the recommended next best dose. Here, the MTD is defined as the dose that reaches a specific target probability of the occurrence of a DLT.

Usage

StoppingMTDdistribution(
  target = 0.33,
  thresh = 0.5,
  prob = 0.9,
  report_label = NA_character_
)

.DefaultStoppingMTDdistribution()
StoppingMTDdistribution(
  target = 0.33,
  thresh = 0.5,
  prob = 0.9,
  report_label = NA_character_
)

.DefaultStoppingMTDdistribution()

Arguments

target

(proportion)
see slot definition.

thresh

(proportion)
see slot definition.

prob

(proportion)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

target: (proportion)
the target toxicity probability (except 0 or 1) defining the MTD.
thresh: (proportion)
the threshold (except 0 or 1) relative to the recommended next best dose.
prob: (proportion)
required minimum probability, except 0 or 1.

Note

Typically, end users will not use the .DefaultStoppingMTDDistribution() function.

Examples

# As example, here is the rule for stopping the study if there is at least 0.9
# probability that MTD > 0.5 * next_dose. Here MTD is defined as the dose for
# which prob(DLT) = 0.33
my_stopping <- StoppingMTDdistribution(
  target = 0.33,
  thresh = 0.5,
  prob = 0.9
)
# As example, here is the rule for stopping the study if there is at least 0.9
# probability that MTD > 0.5 * next_dose. Here MTD is defined as the dose for
# which prob(DLT) = 0.33
my_stopping <- StoppingMTDdistribution(
  target = 0.33,
  thresh = 0.5,
  prob = 0.9
)

`StoppingOrdinal`

Description

StoppingOrdinal is the class for stopping based on a Stopping rule applied to a specific toxicity grade in an ordinal CRM trial

Usage

StoppingOrdinal(grade, rule)

.DefaultStoppingOrdinal()
StoppingOrdinal(grade, rule)

.DefaultStoppingOrdinal()

Arguments

grade

(integer)
see slot definition.

rule

(Stopping)
see slot definition.

Slots

grade: (integer)
the grade to which the rule should be applied
rule: (Stopping)
the rule to apply

Note

Typically, end users will not use the .DefaultStoppingOrdinal() function.

Examples

StoppingOrdinal(
  1L,
  StoppingTargetProb(target = c(0.2, 0.35), prob = 0.6)
)
StoppingOrdinal(
  1L,
  StoppingTargetProb(target = c(0.2, 0.35), prob = 0.6)
)

`StoppingPatientsNearDose`

Description

StoppingPatientsNearDose is the class for stopping based on number of patients near to next best dose.

Usage

StoppingPatientsNearDose(
  nPatients = 10L,
  percentage = 50,
  include_backfill = TRUE,
  report_label = NA_character_
)

.DefaultStoppingPatientsNearDose()
StoppingPatientsNearDose(
  nPatients = 10L,
  percentage = 50,
  include_backfill = TRUE,
  report_label = NA_character_
)

.DefaultStoppingPatientsNearDose()

Arguments

nPatients

(number)
see slot definition.

percentage

(number)
see slot definition.

include_backfill

(flag)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

nPatients: (number)
number of required patients.
percentage: (number)
percentage (between and including 0 and 100) within the next best dose the patients must lie.
include_backfill: (flag)
whether to include backfill patients.

Note

Typically, end users will not use the .DefaultStoppingPatientsNearDose() function.

Examples

# As example, here is the rule for stopping the study if at least 9 patients
# were dosed at a dose within (1 +/- 0.2) of the next best dose.

my_stopping <- StoppingPatientsNearDose(
  nPatients = 9,
  percentage = 20
)

# Variant which excludes backfill cohorts.

my_stopping <- StoppingPatientsNearDose(
  nPatients = 9,
  percentage = 20,
  include_backfill = FALSE
)
# As example, here is the rule for stopping the study if at least 9 patients
# were dosed at a dose within (1 +/- 0.2) of the next best dose.

my_stopping <- StoppingPatientsNearDose(
  nPatients = 9,
  percentage = 20
)

# Variant which excludes backfill cohorts.

my_stopping <- StoppingPatientsNearDose(
  nPatients = 9,
  percentage = 20,
  include_backfill = FALSE
)

`StoppingSpecificDose`

Description

StoppingSpecificDose is the class for testing a stopping rule at specific dose of the dose grid and not at the next best dose.

Usage

StoppingSpecificDose(
  rule = StoppingTargetProb(target = c(0, 0.3), prob = 0.8),
  dose = 80,
  report_label = NA_character_
)

.DefaultStoppingSpecificDose()
StoppingSpecificDose(
  rule = StoppingTargetProb(target = c(0, 0.3), prob = 0.8),
  dose = 80,
  report_label = NA_character_
)

.DefaultStoppingSpecificDose()

Arguments

rule

(Stopping)
see slot definition.

dose

(number)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

rule: (Stopping)
a stopping rule available in this package.
dose: (positive_number)
a dose that is defined as part of the dose grid of the data.

Note

Typically, end users will not use the .DefaultStoppingSpecificDose() function.

Examples

# Stop if highest dose 80 is safe.
highest_dose_safe <- StoppingSpecificDose(
  rule = StoppingTargetProb(target = c(0, 0.3), prob = 0.8),
  dose = 80
)

# Stop if lowest dose 10 is toxic.
lowest_dose_toxic <- StoppingSpecificDose(
  rule = StoppingTargetProb(target = c(0.3, 1), prob = 0.8),
  dose = 10
)
# Stop if highest dose 80 is safe.
highest_dose_safe <- StoppingSpecificDose(
  rule = StoppingTargetProb(target = c(0, 0.3), prob = 0.8),
  dose = 80
)

# Stop if lowest dose 10 is toxic.
lowest_dose_toxic <- StoppingSpecificDose(
  rule = StoppingTargetProb(target = c(0.3, 1), prob = 0.8),
  dose = 10
)

`StoppingTargetBiomarker`

Description

StoppingTargetBiomarker is a class for stopping based on probability of target biomarker.

Usage

StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.3,
  is_relative = TRUE,
  report_label = NA_character_
)

.DefaultStoppingTargetBiomarker()
StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.3,
  is_relative = TRUE,
  report_label = NA_character_
)

.DefaultStoppingTargetBiomarker()

Arguments

target

(numeric)
see slot definition.

prob

(proportion)
see slot definition.

is_relative

(flag)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

target: (numeric)
the biomarker target range that needs to be reached. For example, target = c(0.8, 1.0) with is_relative = TRUE means that we target a dose with at least 80% of maximum biomarker level.
is_relative: (flag)
is target relative? If it so (default), then the target is interpreted relative to the maximum, so it must be a probability range. Otherwise, the target is interpreted as absolute biomarker range.
prob: (proportion)
required target probability (except 0 or 1) for reaching sufficient precision.

Note

Typically, end users will not use the .DefaultStoppingTargetBiomarker() function.

Examples

# Stopping the study if there is at least 0.5 probability that the biomarker
# (efficacy) is within the biomarker target range of [0.9, 1.0] (relative to the
# maximum for the biomarker).

my_stopping <- StoppingTargetBiomarker(target = c(0.9, 1), prob = 0.5)
# Stopping the study if there is at least 0.5 probability that the biomarker
# (efficacy) is within the biomarker target range of [0.9, 1.0] (relative to the
# maximum for the biomarker).

my_stopping <- StoppingTargetBiomarker(target = c(0.9, 1), prob = 0.5)

`StoppingTargetProb`

Description

StoppingTargetProb is the class for stopping based on the probability of the DLT rate being in the target toxicity interval.

Usage

StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.4,
  report_label = NA_character_
)

.DefaultStoppingTargetProb()
StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.4,
  report_label = NA_character_
)

.DefaultStoppingTargetProb()

Arguments

target

(number)
see slot definition.

prob

(proportion)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

target: (number)
the target toxicity interval, e.g. c(0.2, 0.35).
prob: (proportion)
required target toxicity probability (except 0 or 1) for reaching sufficient precision.

Note

Typically, end users will not use the .DefaultStoppingTargetProb() function.

Examples

# As example, here is the rule for stopping the study if the posterior
# probability that [0.2 =< Prob(DLT | dose) <= 0.35] for the next best dose
# is above 0.5.
my_stopping <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
# As example, here is the rule for stopping the study if the posterior
# probability that [0.2 =< Prob(DLT | dose) <= 0.35] for the next best dose
# is above 0.5.
my_stopping <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

`StoppingTDCIRatio`

Description

StoppingTDCIRatio is the class for testing a stopping rule that is based on a target ratio of the 95% credibility interval. Specifically, this is the ratio of the upper to the lower bound of the 95% credibility interval's estimate of the target dose (i.e. a dose that corresponds to a given target probability of the occurrence of a DLT prob_target).

Usage

StoppingTDCIRatio(
  target_ratio = 5,
  prob_target = 0.3,
  report_label = NA_character_
)

.DefaultStoppingTDCIRatio()
StoppingTDCIRatio(
  target_ratio = 5,
  prob_target = 0.3,
  report_label = NA_character_
)

.DefaultStoppingTDCIRatio()

Arguments

target_ratio

(numeric)
see slot definition.

prob_target

(proportion)
see slot definition.

report_label

(string or NA)
see slot definition.

Slots

target_ratio: (numeric)
target for the ratio of the 95% credibility interval's estimate, that is required to stop a trial.
prob_target: (proportion)
the target probability of the occurrence of a DLT.

Note

Typically, end users will not use the .DefaultStoppingTDCIRatio() function.

Examples

# Define the target stopping ratio (5) and the target probability of a DLT to
# be used (0.3).
my_stopping <- StoppingTDCIRatio(
  target_ratio = 5,
  prob_target = 0.3
)
# Define the target stopping ratio (5) and the target probability of a DLT to
# be used (0.3).
my_stopping <- StoppingTDCIRatio(
  target_ratio = 5,
  prob_target = 0.3
)

Stop the trial?

Description

This function returns whether to stop the trial.

Stopping rule based precision of the MTD estimation. The trial is stopped, when the MTD can be estimated with sufficient precision. The criteria is based on the robust coefficient of variation (CV) calculated from the posterior distribution. The robust CV is defined mad(MTD) / median(MTD), where mad is the median absolute deviation.

Stopping based based on the lowest non placebo dose. The trial is stopped when the lowest non placebo dose meets the Hard Safety Rule, i.e. it is deemed to be overly toxic. Stopping is based on the observed data at the lowest dose level using a Bin-Beta model based on DLT probability.

Usage

stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMissingDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingList,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingAll,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingAny,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingCohortsNearDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingPatientsNearDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMinCohorts,ANY,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMinPatients,ANY,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingTargetProb,numeric,Samples,GeneralModel,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingMTDdistribution,numeric,Samples,GeneralModel,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMTDCV,numeric,Samples,GeneralModel,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingLowestDoseHSRBeta,numeric,Samples,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingTargetBiomarker,numeric,Samples,DualEndpoint,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingSpecificDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingHighestDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingOrdinal,numeric,ANY,LogisticLogNormalOrdinal,DataOrdinal'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingOrdinal,numeric,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingExternal,numeric,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, external, ...)

## S4 method for signature 'StoppingTDCIRatio,ANY,Samples,ModelTox,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingTDCIRatio,ANY,missing,ModelTox,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingMaxGainCIRatio,ANY,Samples,ModelTox,DataDual'
stopTrial(
  stopping,
  dose,
  samples,
  model,
  data,
  TDderive,
  Effmodel,
  Effsamples,
  Gstarderive,
  ...
)

## S4 method for signature 
## 'StoppingMaxGainCIRatio,ANY,missing,ModelTox,DataDual'
stopTrial(stopping, dose, model, data, Effmodel, ...)
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMissingDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingList,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingAll,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingAny,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingCohortsNearDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingPatientsNearDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMinCohorts,ANY,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMinPatients,ANY,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingTargetProb,numeric,Samples,GeneralModel,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingMTDdistribution,numeric,Samples,GeneralModel,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMTDCV,numeric,Samples,GeneralModel,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingLowestDoseHSRBeta,numeric,Samples,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingTargetBiomarker,numeric,Samples,DualEndpoint,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingSpecificDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingHighestDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingOrdinal,numeric,ANY,LogisticLogNormalOrdinal,DataOrdinal'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingOrdinal,numeric,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingExternal,numeric,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, external, ...)

## S4 method for signature 'StoppingTDCIRatio,ANY,Samples,ModelTox,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingTDCIRatio,ANY,missing,ModelTox,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingMaxGainCIRatio,ANY,Samples,ModelTox,DataDual'
stopTrial(
  stopping,
  dose,
  samples,
  model,
  data,
  TDderive,
  Effmodel,
  Effsamples,
  Gstarderive,
  ...
)

## S4 method for signature 
## 'StoppingMaxGainCIRatio,ANY,missing,ModelTox,DataDual'
stopTrial(stopping, dose, model, data, Effmodel, ...)

Arguments

stopping

(Stopping)
the rule for stopping the trial.

dose

the recommended next best dose.

samples

(Samples)
the mcmc samples.

model

(GeneralModel)
the model.

data

(Data)
input data.

...

additional arguments without method dispatch.

external

(flag)
whether to stop based on the external result or not.

TDderive

(function)
the function which derives from the input, a vector of the posterior samples called TDsamples of the dose which has the probability of the occurrence of DLE equals to either the targetDuringTrial or targetEndOfTrial, the final next best TDtargetDuringTrial (the dose with probability of the occurrence of DLE equals to the targetDuringTrial) and TDtargetEndOfTrial estimate.

Effmodel

(ModelEff)
the efficacy model.

Effsamples

(Samples)
the efficacy samples.

Gstarderive

(function)
the function which derives from the input, a vector of the posterior Gstar (the dose which gives the maximum gain value) samples called Gstarsamples, the final next best Gstar estimate.

Value

logical value: TRUE if the trial can be stopped, FALSE otherwise. It should have an attribute message which gives the reason for the decision.

Functions

stopTrial( stopping = StoppingMissingDose, dose = numeric, samples = ANY, model = ANY, data = Data ): Stop based on value returned by next best dose.
stopTrial( stopping = StoppingList, dose = ANY, samples = ANY, model = ANY, data = ANY ): Stop based on multiple stopping rules.
stopTrial( stopping = StoppingAll, dose = ANY, samples = ANY, model = ANY, data = ANY ): Stop based on fulfillment of all multiple stopping rules.
stopTrial( stopping = StoppingAny, dose = ANY, samples = ANY, model = ANY, data = ANY ): Stop based on fulfillment of any stopping rule.
stopTrial( stopping = StoppingCohortsNearDose, dose = numeric, samples = ANY, model = ANY, data = Data ): Stop based on number of cohorts near to next best dose.
stopTrial( stopping = StoppingPatientsNearDose, dose = numeric, samples = ANY, model = ANY, data = Data ): Stop based on number of patients near to next best dose.
stopTrial( stopping = StoppingMinCohorts, dose = ANY, samples = ANY, model = ANY, data = Data ): Stop based on minimum number of cohorts.
stopTrial( stopping = StoppingMinPatients, dose = ANY, samples = ANY, model = ANY, data = Data ): Stop based on minimum number of patients.
stopTrial( stopping = StoppingTargetProb, dose = numeric, samples = Samples, model = GeneralModel, data = ANY ): Stop based on probability of target tox interval
stopTrial( stopping = StoppingMTDdistribution, dose = numeric, samples = Samples, model = GeneralModel, data = ANY ): Stop based on MTD distribution.
stopTrial( stopping = StoppingTargetBiomarker, dose = numeric, samples = Samples, model = DualEndpoint, data = ANY ): Stop based on probability of targeting biomarker
stopTrial( stopping = StoppingSpecificDose, dose = numeric, samples = ANY, model = ANY, data = Data ): if Stopping rule is met for specific dose of the planned dose grid and not just for the default next best dose.
stopTrial( stopping = StoppingHighestDose, dose = numeric, samples = ANY, model = ANY, data = Data ): Stop when the highest dose is reached.
stopTrial( stopping = StoppingOrdinal, dose = numeric, samples = ANY, model = LogisticLogNormalOrdinal, data = DataOrdinal ): Stop based on value returned by next best dose.
stopTrial( stopping = StoppingOrdinal, dose = numeric, samples = ANY, model = ANY, data = ANY ): Stop based on value returned by next best dose.
stopTrial( stopping = StoppingExternal, dose = numeric, samples = ANY, model = ANY, data = ANY ): Stop based on an external flag.
stopTrial( stopping = StoppingTDCIRatio, dose = ANY, samples = Samples, model = ModelTox, data = ANY ): Stop based on StoppingTDCIRatio class when reaching the target ratio of the upper to the lower 95% credibility interval of the estimate (TDtargetEndOfTrial). This is a stopping rule which incorporates only DLE responses and DLE samples are given.
stopTrial( stopping = StoppingTDCIRatio, dose = ANY, samples = missing, model = ModelTox, data = ANY ): Stop based on StoppingTDCIRatio class when reaching the target ratio of the upper to the lower 95% credibility interval of the estimate (TDtargetEndOfTrial). This is a stopping rule which incorporates only DLE responses and no DLE samples are involved.
stopTrial( stopping = StoppingMaxGainCIRatio, dose = ANY, samples = Samples, model = ModelTox, data = DataDual ): Stop based on reaching the target ratio of the upper to the lower 95% credibility interval of the estimate (the minimum of Gstar and TDtargetEndOfTrial). This is a stopping rule which incorporates DLE and efficacy responses and DLE and efficacy samples are also used.
stopTrial( stopping = StoppingMaxGainCIRatio, dose = ANY, samples = missing, model = ModelTox, data = DataDual ): Stop based on reaching the target ratio of the upper to the lower 95% credibility interval of the estimate (the minimum of Gstar and TDtargetEndOfTrial). This is a stopping rule which incorporates DLE and efficacy responses without DLE and efficacy samples involved.

Examples

## Example of combining stopping rules with '&' and/or '|' operators

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)

myStopping <- (myStopping1 & myStopping2) | myStopping3
# Example of usage for `StoppingMissingDose` StopTrial class.

# Create the data.
my_data <- Data(
  x = c(0.01, 0.1, 0.5, 3, 6, 10, 10, 10),
  y = c(0, 1, 1, 0, 0, 0, 0, 1),
  cohort = c(1, 1, 2, 3, 4, 5, 5, 5),
  ID = 1:8,
  doseGrid = c(
    0.01,
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  placebo = TRUE
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose based
# on the class 'NextBestNCRM'.
my_next_best <- NextBestNCRM(
  target = c(0.1, 0.25),
  overdose = c(0.2, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  my_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# Define the stopping rule such that the study would be stopped if there is
# no safe active dose returned from dose_recommendation.
my_stopping <- StoppingMissingDose()
my_stopping <- StoppingAny(
  stop_list = c(
    StoppingMinPatients(nPatients = 16),
    StoppingMissingDose()
  )
)

# Evaluate if to stop the trial.
stopTrial(
  stopping = my_stopping,
  dose = dose_recommendation$value,
  data = my_data,
  model = my_model
)
# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)

# Create a list of stopping rules (of class 'StoppingList') which will then be
# summarized (in this specific example) with the 'any' function, meaning that the study
# would be stopped if 'any' of the single stopping rules is TRUE.
mystopping <- StoppingList(
  stop_list = c(myStopping1, myStopping2, myStopping3),
  summary = any
)

# Evaluate if to stop the Trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)

# Combine the stopping rules, obtaining (in this specific example) a list of stopping
# rules of class 'StoppingAll'
myStopping <- (myStopping1 | myStopping2) & myStopping3

# Evaluate if to stop the Trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)

# Combine the stopping rules, obtaining (in this specific example) a list of stopping
# rules of class 'StoppingAny'
myStopping <- (myStopping1 | myStopping2) | myStopping3

# Evaluate if to stop the Trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if at least 3
# cohorts were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingCohortsNearDose(nCohorts = 3, percentage = 0.2)

# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if at least 9
# patients were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingPatientsNearDose(nPatients = 9, percentage = 0.2)

# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if at least 6
# cohorts were already dosed
myStopping <- StoppingMinCohorts(nCohorts = 6)

# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if at least 20
# patients were already dosed
myStopping <- StoppingMinPatients(nPatients = 20)

# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if there is at least
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35]
myStopping <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

# Evaluate if to stop the trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if there is at least
# 0.9 probability that MTD > 0.5*next_best_dose. Here MTD is defined as the dose for
# which prob(DLE)=0.33
myStopping <- StoppingMTDdistribution(target = 0.33, thresh = 0.5, prob = 0.9)

# Evaluate if to stop the trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  my_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# Define the stopping rule such that the study would be stopped if the
# the MTD can be estimated with sufficient precision, i.e. if robust coefficient
# of variation is below 40%.
my_stopping <- StoppingMTDCV(target = 0.3, thresh_cv = 40)

# Evaluate if to stop the trial.
stopTrial(
  stopping = my_stopping,
  dose = dose_recommendation$value,
  samples = my_samples,
  model = my_model,
  data = my_data
)
# Create the data.
data <- Data(
  x = c(0.1, 0.1, 0.1),
  y = c(0, 0, 1),
  cohort = c(1, 1, 1),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  ID = 1:3
)

# Initialize the CRM model used to model the data.
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)

samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

next_max_dose <- maxDose(my_increments, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  my_next_best,
  doselimit = next_max_dose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if first dose
# is toxic based on a Beta posterior distribution with Beta(1,1) prior.
my_stopping <- StoppingLowestDoseHSRBeta(
  target = 0.3,
  prob = 0.9
)

# Evaluate if the trial will be stopped.
stopTrial(
  stopping = my_stopping,
  dose = dose_recommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint start

# Create the data
data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)

samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# In this case target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)
myNextBest <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if if there is at
# least 0.5 posterior probability that the biomarker (efficacy) is within the
# biomarker target range of [0.9, 1.0] (relative to the maximum for the biomarker).
myStopping <- StoppingTargetBiomarker(target = c(0.9, 1), prob = 0.5)

# Evaluate if to stop the trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# Create some data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'.
my_nb_ncrm <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
my_dose_recommendation <- nextBest(
  nextBest = my_nb_ncrm,
  doselimit = 100,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# Define the stopping rules.
highest_dose_safe <- StoppingSpecificDose(
  rule = StoppingTargetProb(target = c(0, 0.3), prob = 0.8),
  dose = 80
)
max_patients <- StoppingMinPatients(nPatients = 20)
patients_near_dose <- StoppingPatientsNearDose(nPatients = 3, percentage = 0)

# Create a list of stopping rules (of class 'StoppingList') which will then be
# summarized (in this specific example) with the 'any' function, meaning that
# the study would be stopped if 'any' of the single stopping rules is TRUE.
my_stopping <- highest_dose_safe | max_patients | patients_near_dose

# Evaluate if to stop the Trial
stopTrial(
  stopping = my_stopping,
  dose = my_dose_recommendation$value,
  samples = my_samples,
  model = my_model,
  data = my_data
)
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 80, 80, 80),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if there is at least
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35]
stopTarget <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

## now use the StoppingHighestDose rule:
stopHigh <-
  StoppingHighestDose() &
  StoppingPatientsNearDose(nPatients = 3, percentage = 0) &
  StoppingTargetProb(target = c(0, 0.2), prob = 0.5)

## and combine everything:
myStopping <- stopTarget | stopHigh

# Then evaluate if to stop the trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
data <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
samples <- mcmc(data, model, options)

myIncrements <- .DefaultIncrementsOrdinal()
nextMaxDose <- maxDose(myIncrements, data = data)

myNextBest <- .DefaultNextBestOrdinal()

doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

myStopping <- .DefaultStoppingOrdinal()

stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)
data <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
samples <- mcmc(data, model, options)

myIncrements <- .DefaultIncrementsOrdinal()
nextMaxDose <- maxDose(myIncrements, data = data)

myNextBest <- .DefaultNextBestOrdinal()

doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

myStopping <- .DefaultStoppingOrdinal()

stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)
my_rule <- StoppingExternal(report_label = "Based on combo stop")

stopTrial(
  my_rule,
  5,
  .DefaultSamples(),
  .DefaultModelLogNormal(),
  .DefaultData(),
  external = TRUE
)
# nolint start

##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses with samples
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

##model can be specified of 'Model' or 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
##define MCMC options
## for illustration purposes, in reality larger number of burnin and samples shoud be used
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
##samples of 'Samples' class
samples <- mcmc(data, model, options)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

RecommendDose <- nextBest(
  tdNextBest,
  doselimit = max(data@doseGrid),
  samples = samples,
  model = model,
  data = data
)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(
  stopping = myStopping,
  dose = RecommendDose$next_dose_drt,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# nolint start

##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

##model must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest <- NextBestTD(prob_target_drt = 0.35, prob_target_eot = 0.3)


RecommendDose <- nextBest(
  tdNextBest,
  doselimit = max(data@doseGrid),
  model = model,
  data = data
)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(
  stopping = myStopping,
  dose = RecommendDose$next_dose_drt,
  model = model,
  data = data
)

# nolint end
# nolint start
##define the stopping rules based on the 'StoppingMaxGainCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)

##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

##Effmodel must be  of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
##for illustration purposes, in reality larger number of burnin and samples shoud be used
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
##DLE and efficacy samples must be of 'Samples' class

DLEsamples <- mcmc(data, DLEmodel, options)
Effsamples <- mcmc(data, Effmodel, options)

##define the 'StoppingMaxGainCIRatio' class
myStopping <- StoppingMaxGainCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

RecommendDose <- nextBest(
  mynextbest,
  doselimit = max(data@doseGrid),
  samples = DLEsamples,
  model = DLEmodel,
  data = data,
  model_eff = Effmodel,
  samples_eff = Effsamples
)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(
  stopping = myStopping,
  dose = RecommendDose$next_dose,
  samples = DLEsamples,
  model = DLEmodel,
  data = data,
  TDderive = function(TDsamples) {
    quantile(TDsamples, prob = 0.3)
  },
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  Gstarderive = function(Gstarsamples) {
    quantile(Gstarsamples, prob = 0.5)
  }
)

# nolint end
# nolint start

##define the stopping rules based on the 'StoppingMaxGainCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)

##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

##Effmodel must be  of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)


##define the 'StoppingMaxGainCIRatio' class
myStopping <- StoppingMaxGainCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest <- NextBestMaxGain(prob_target_drt = 0.35, prob_target_eot = 0.3)

RecommendDose <- nextBest(
  mynextbest,
  doselimit = max(data@doseGrid),
  model = DLEmodel,
  model_eff = Effmodel,
  data = data
)

##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(
  stopping = myStopping,
  dose = RecommendDose$next_dose,
  model = DLEmodel,
  data = data,
  Effmodel = Effmodel
)

# nolint end
## Example of combining stopping rules with '&' and/or '|' operators

myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)

myStopping <- (myStopping1 & myStopping2) | myStopping3
# Example of usage for `StoppingMissingDose` StopTrial class.

# Create the data.
my_data <- Data(
  x = c(0.01, 0.1, 0.5, 3, 6, 10, 10, 10),
  y = c(0, 1, 1, 0, 0, 0, 0, 1),
  cohort = c(1, 1, 2, 3, 4, 5, 5, 5),
  ID = 1:8,
  doseGrid = c(
    0.01,
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  placebo = TRUE
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose based
# on the class 'NextBestNCRM'.
my_next_best <- NextBestNCRM(
  target = c(0.1, 0.25),
  overdose = c(0.2, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  my_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# Define the stopping rule such that the study would be stopped if there is
# no safe active dose returned from dose_recommendation.
my_stopping <- StoppingMissingDose()
my_stopping <- StoppingAny(
  stop_list = c(
    StoppingMinPatients(nPatients = 16),
    StoppingMissingDose()
  )
)

# Evaluate if to stop the trial.
stopTrial(
  stopping = my_stopping,
  dose = dose_recommendation$value,
  data = my_data,
  model = my_model
)
# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)

# Create a list of stopping rules (of class 'StoppingList') which will then be
# summarized (in this specific example) with the 'any' function, meaning that the study
# would be stopped if 'any' of the single stopping rules is TRUE.
mystopping <- StoppingList(
  stop_list = c(myStopping1, myStopping2, myStopping3),
  summary = any
)

# Evaluate if to stop the Trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)

# Combine the stopping rules, obtaining (in this specific example) a list of stopping
# rules of class 'StoppingAll'
myStopping <- (myStopping1 | myStopping2) & myStopping3

# Evaluate if to stop the Trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# nolint start

# Create some data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)
myStopping3 <- StoppingMinPatients(nPatients = 20)

# Combine the stopping rules, obtaining (in this specific example) a list of stopping
# rules of class 'StoppingAny'
myStopping <- (myStopping1 | myStopping2) | myStopping3

# Evaluate if to stop the Trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if at least 3
# cohorts were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingCohortsNearDose(nCohorts = 3, percentage = 0.2)

# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if at least 9
# patients were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingPatientsNearDose(nPatients = 9, percentage = 0.2)

# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if at least 6
# cohorts were already dosed
myStopping <- StoppingMinCohorts(nCohorts = 6)

# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if at least 20
# patients were already dosed
myStopping <- StoppingMinPatients(nPatients = 20)

# Evaluate if to stop the trial
stopTrial(stopping = myStopping, dose = doseRecommendation$value, data = data)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if there is at least
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35]
myStopping <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

# Evaluate if to stop the trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if there is at least
# 0.9 probability that MTD > 0.5*next_best_dose. Here MTD is defined as the dose for
# which prob(DLE)=0.33
myStopping <- StoppingMTDdistribution(target = 0.33, thresh = 0.5, prob = 0.9)

# Evaluate if to stop the trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# Create the data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
next_max_dose <- maxDose(my_increments, data = my_data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  my_next_best,
  doselimit = next_max_dose,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# Define the stopping rule such that the study would be stopped if the
# the MTD can be estimated with sufficient precision, i.e. if robust coefficient
# of variation is below 40%.
my_stopping <- StoppingMTDCV(target = 0.3, thresh_cv = 40)

# Evaluate if to stop the trial.
stopTrial(
  stopping = my_stopping,
  dose = dose_recommendation$value,
  samples = my_samples,
  model = my_model,
  data = my_data
)
# Create the data.
data <- Data(
  x = c(0.1, 0.1, 0.1),
  y = c(0, 0, 1),
  cohort = c(1, 1, 1),
  doseGrid = c(
    0.1,
    0.5,
    1.5,
    3,
    6,
    seq(from = 10, to = 80, by = 2)
  ),
  ID = 1:3
)

# Initialize the CRM model used to model the data.
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior.
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)

samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

next_max_dose <- maxDose(my_increments, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'.
my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
dose_recommendation <- nextBest(
  my_next_best,
  doselimit = next_max_dose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if first dose
# is toxic based on a Beta posterior distribution with Beta(1,1) prior.
my_stopping <- StoppingLowestDoseHSRBeta(
  target = 0.3,
  prob = 0.9
)

# Evaluate if the trial will be stopped.
stopTrial(
  stopping = my_stopping,
  dose = dose_recommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint start

# Create the data
data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 50, 50, 50),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1),
  w = c(
    0.31,
    0.42,
    0.59,
    0.45,
    0.6,
    0.7,
    0.55,
    0.6,
    0.52,
    0.54,
    0.56,
    0.43,
    0.41,
    0.39,
    0.34,
    0.38,
    0.21
  ),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)

samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# In this case target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)
myNextBest <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if if there is at
# least 0.5 posterior probability that the biomarker (efficacy) is within the
# biomarker target range of [0.9, 1.0] (relative to the maximum for the biomarker).
myStopping <- StoppingTargetBiomarker(target = c(0.9, 1), prob = 0.5)

# Evaluate if to stop the trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# Create some data.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  ID = 1:8,
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data.
my_model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 50
)

# Set-up some MCMC parameters and generate samples from the posterior.
my_options <- McmcOptions(burnin = 5, step = 1, samples = 10)
my_samples <- mcmc(my_data, my_model, my_options)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'.
my_nb_ncrm <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose.
my_dose_recommendation <- nextBest(
  nextBest = my_nb_ncrm,
  doselimit = 100,
  samples = my_samples,
  model = my_model,
  data = my_data
)

# Define the stopping rules.
highest_dose_safe <- StoppingSpecificDose(
  rule = StoppingTargetProb(target = c(0, 0.3), prob = 0.8),
  dose = 80
)
max_patients <- StoppingMinPatients(nPatients = 20)
patients_near_dose <- StoppingPatientsNearDose(nPatients = 3, percentage = 0)

# Create a list of stopping rules (of class 'StoppingList') which will then be
# summarized (in this specific example) with the 'any' function, meaning that
# the study would be stopped if 'any' of the single stopping rules is TRUE.
my_stopping <- highest_dose_safe | max_patients | patients_near_dose

# Evaluate if to stop the Trial
stopTrial(
  stopping = my_stopping,
  dose = my_dose_recommendation$value,
  samples = my_samples,
  model = my_model,
  data = my_data
)
# nolint start

# Create the data
data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40, 80, 80, 80),
  y = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
  cohort = c(0, 1, 2, 3, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the stopping rule such that the study would be stopped if there is at least
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35]
stopTarget <- StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)

## now use the StoppingHighestDose rule:
stopHigh <-
  StoppingHighestDose() &
  StoppingPatientsNearDose(nPatients = 3, percentage = 0) &
  StoppingTargetProb(target = c(0, 0.2), prob = 0.5)

## and combine everything:
myStopping <- stopTarget | stopHigh

# Then evaluate if to stop the trial
stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)

# nolint end
data <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
samples <- mcmc(data, model, options)

myIncrements <- .DefaultIncrementsOrdinal()
nextMaxDose <- maxDose(myIncrements, data = data)

myNextBest <- .DefaultNextBestOrdinal()

doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

myStopping <- .DefaultStoppingOrdinal()

stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)
data <- .DefaultDataOrdinal()
model <- .DefaultLogisticLogNormalOrdinal()
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
samples <- mcmc(data, model, options)

myIncrements <- .DefaultIncrementsOrdinal()
nextMaxDose <- maxDose(myIncrements, data = data)

myNextBest <- .DefaultNextBestOrdinal()

doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

myStopping <- .DefaultStoppingOrdinal()

stopTrial(
  stopping = myStopping,
  dose = doseRecommendation$value,
  samples = samples,
  model = model,
  data = data
)
my_rule <- StoppingExternal(report_label = "Based on combo stop")

stopTrial(
  my_rule,
  5,
  .DefaultSamples(),
  .DefaultModelLogNormal(),
  .DefaultData(),
  external = TRUE
)
# nolint start

##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses with samples
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

##model can be specified of 'Model' or 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
##define MCMC options
## for illustration purposes, in reality larger number of burnin and samples shoud be used
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
##samples of 'Samples' class
samples <- mcmc(data, model, options)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

RecommendDose <- nextBest(
  tdNextBest,
  doselimit = max(data@doseGrid),
  samples = samples,
  model = model,
  data = data
)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(
  stopping = myStopping,
  dose = RecommendDose$next_dose_drt,
  samples = samples,
  model = model,
  data = data
)

# nolint end
# nolint start

##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses
## we need a data object with doses >= 1:
data <- Data(
  x = c(25, 50, 50, 75, 150, 200, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  doseGrid = seq(from = 25, to = 300, by = 25)
)

##model must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest <- NextBestTD(prob_target_drt = 0.35, prob_target_eot = 0.3)


RecommendDose <- nextBest(
  tdNextBest,
  doselimit = max(data@doseGrid),
  model = model,
  data = data
)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(
  stopping = myStopping,
  dose = RecommendDose$next_dose_drt,
  model = model,
  data = data
)

# nolint end
# nolint start
##define the stopping rules based on the 'StoppingMaxGainCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)

##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

##Effmodel must be  of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)
##for illustration purposes, in reality larger number of burnin and samples shoud be used
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
##DLE and efficacy samples must be of 'Samples' class

DLEsamples <- mcmc(data, DLEmodel, options)
Effsamples <- mcmc(data, Effmodel, options)

##define the 'StoppingMaxGainCIRatio' class
myStopping <- StoppingMaxGainCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

RecommendDose <- nextBest(
  mynextbest,
  doselimit = max(data@doseGrid),
  samples = DLEsamples,
  model = DLEmodel,
  data = data,
  model_eff = Effmodel,
  samples_eff = Effsamples
)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(
  stopping = myStopping,
  dose = RecommendDose$next_dose,
  samples = DLEsamples,
  model = DLEmodel,
  data = data,
  TDderive = function(TDsamples) {
    quantile(TDsamples, prob = 0.3)
  },
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  Gstarderive = function(Gstarsamples) {
    quantile(Gstarsamples, prob = 0.5)
  }
)

# nolint end
# nolint start

##define the stopping rules based on the 'StoppingMaxGainCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)

##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

##Effmodel must be  of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)


##define the 'StoppingMaxGainCIRatio' class
myStopping <- StoppingMaxGainCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest <- NextBestMaxGain(prob_target_drt = 0.35, prob_target_eot = 0.3)

RecommendDose <- nextBest(
  mynextbest,
  doselimit = max(data@doseGrid),
  model = DLEmodel,
  model_eff = Effmodel,
  data = data
)

##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(
  stopping = myStopping,
  dose = RecommendDose$next_dose,
  model = DLEmodel,
  data = data,
  Effmodel = Effmodel
)

# nolint end

Subsetting Operator for the Data Class

Description

Subset observations (patients) from a Data object using numeric or logical indexing.

Usage

## S4 method for signature 'Data,numeric,missing,missing'
x[i]

## S4 method for signature 'Data,logical,missing,missing'
x[i]
## S4 method for signature 'Data,numeric,missing,missing'
x[i]

## S4 method for signature 'Data,logical,missing,missing'
x[i]

Arguments

x

(Data)
what to subset.

i

(integer or logical)
indices or logical vector for subsetting observations.

Value

A Data object with the selected observations.

Summarize Dual-Endpoint Design Simulations

Description

Summarize the dual-endpoint design simulations, relative to given true dose-toxicity and dose-biomarker curves.

Usage

## S4 method for signature 'DualSimulations'
summary(object, trueTox, trueBiomarker, target = c(0.2, 0.35), ...)
## S4 method for signature 'DualSimulations'
summary(object, trueTox, trueBiomarker, target = c(0.2, 0.35), ...)

Arguments

object

(DualSimulations)
the object we want to summarize.

trueTox

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity.

trueBiomarker

(function)
a function which takes as input a dose (vector) and returns the true biomarker level (vector).

target

(numeric)
the target toxicity interval (default: 20-35%) used for the computations.

...

additional arguments can be supplied here for trueTox and trueBiomarker.

Value

An object of class DualSimulationsSummary.

Examples

# Define the dose-grid.
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))


# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for stopping.
my_stopping1 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)

# For illustration stop with 6 subjects.
my_stopping <- my_stopping1 | StoppingMinPatients(6) | StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
design <- DualDesign(
  model = my_model,
  data = emptydata,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = CohortSizeConst(3),
  startingDose = 3
)

# Define scenarios for the TRUE toxicity and efficacy profiles.
beta_mod <- function(dose, e0, eMax, delta1, delta2, scal) {
  maxDens <- (delta1^delta1) *
    (delta2^delta2) /
    ((delta1 + delta2)^(delta1 + delta2))
  dose <- dose / scal
  e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
}

true_biomarker <- function(dose) {
  beta_mod(
    dose,
    e0 = 0.2,
    eMax = 0.6,
    delta1 = 5,
    delta2 = 5 * 0.5 / 0.5,
    scal = 100
  )
}

true_tox <- function(dose) {
  pnorm((dose - 60) / 10)
}

# Draw the TRUE profiles.
par(mfrow = c(1, 2))
curve(true_tox(x), from = 0, to = 80)
curve(true_biomarker(x), from = 0, to = 80)

# Run the simulation on the desired design.
# For illustration purposes 1 trial is simulated with 5 burn-ins to generate 20 samples.
my_sims <- simulate(
  object = design,
  trueTox = true_tox,
  trueBiomarker = true_biomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 3,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 5,
    step = 1,
    samples = 20
  )
)

# Summarize the results of the simulations.
summary(
  my_sims,
  trueTox = true_tox,
  trueBiomarker = true_biomarker
)
# Define the dose-grid.
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))


# Initialize the CRM model.
my_model <- DualEndpointRW(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for stopping.
my_stopping1 <- StoppingTargetBiomarker(
  target = c(0.9, 1),
  prob = 0.5
)

# For illustration stop with 6 subjects.
my_stopping <- my_stopping1 | StoppingMinPatients(6) | StoppingMissingDose()

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design.
design <- DualDesign(
  model = my_model,
  data = emptydata,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = CohortSizeConst(3),
  startingDose = 3
)

# Define scenarios for the TRUE toxicity and efficacy profiles.
beta_mod <- function(dose, e0, eMax, delta1, delta2, scal) {
  maxDens <- (delta1^delta1) *
    (delta2^delta2) /
    ((delta1 + delta2)^(delta1 + delta2))
  dose <- dose / scal
  e0 + eMax / maxDens * (dose^delta1) * (1 - dose)^delta2
}

true_biomarker <- function(dose) {
  beta_mod(
    dose,
    e0 = 0.2,
    eMax = 0.6,
    delta1 = 5,
    delta2 = 5 * 0.5 / 0.5,
    scal = 100
  )
}

true_tox <- function(dose) {
  pnorm((dose - 60) / 10)
}

# Draw the TRUE profiles.
par(mfrow = c(1, 2))
curve(true_tox(x), from = 0, to = 80)
curve(true_biomarker(x), from = 0, to = 80)

# Run the simulation on the desired design.
# For illustration purposes 1 trial is simulated with 5 burn-ins to generate 20 samples.
my_sims <- simulate(
  object = design,
  trueTox = true_tox,
  trueBiomarker = true_biomarker,
  sigma2W = 0.01,
  rho = 0,
  nsim = 1,
  parallel = FALSE,
  seed = 3,
  startingDose = 6,
  mcmcOptions = McmcOptions(
    burnin = 5,
    step = 1,
    samples = 20
  )
)

# Summarize the results of the simulations.
summary(
  my_sims,
  trueTox = true_tox,
  trueBiomarker = true_biomarker
)

Summarize the `GeneralSimulations`, Relative to a Given Truth

Description

Summarize simulations relative to a given true dose-toxicity curve.

Usage

## S4 method for signature 'GeneralSimulations'
summary(object, truth, target = c(0.2, 0.35), ...)
## S4 method for signature 'GeneralSimulations'
summary(object, truth, target = c(0.2, 0.35), ...)

Arguments

object

(GeneralSimulations)
the object we want to summarize.

truth

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity.

target

(numeric)
the target toxicity interval (default: 20-35%) used for the computations.

...

additional arguments can be supplied here for truth.

Value

An object of class GeneralSimulationsSummary.

Summarize `PseudoDualFlexiSimulations`

Description

Summary for PseudoDualFlexiSimulations given a pseudo DLE model and the flexible efficacy model.

Usage

## S4 method for signature 'PseudoDualFlexiSimulations'
summary(
  object,
  trueDLE,
  trueEff,
  targetEndOfTrial = 0.3,
  targetDuringTrial = 0.35,
  ...
)
## S4 method for signature 'PseudoDualFlexiSimulations'
summary(
  object,
  trueDLE,
  trueEff,
  targetEndOfTrial = 0.3,
  targetDuringTrial = 0.35,
  ...
)

Arguments

object

(PseudoDualFlexiSimulations)
the object we want to summarize.

trueDLE

(function)
a function which takes as input a dose (vector) and returns the true probability of DLE (vector).

trueEff

(numeric)
a vector which takes as input the true mean efficacy values at all dose levels (in order).

targetEndOfTrial

(number)
the target probability of DLE that are used at the end of a trial. Default at 0.3.

targetDuringTrial

(number)
the target probability of DLE that are used during the trial. Default at 0.35.

...

additional arguments can be supplied here for trueDLE and trueEff.

Value

An object of class PseudoDualSimulationsSummary.

Examples

# nolint start

## If DLE and efficacy responses are considered in the simulations and the 'EffFlexi' class is used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid = seq(25, 300, 25))
## First for the DLE model
## The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## for the efficacy model
Effmodel <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)


## specified the next best
mynextbest <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

## The increments (see Increments class examples)
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)
## cohort size of 3
mySize <- CohortSizeConst(size = 3)
## Stop only when 10 subjects are treated:
## very low sample size is just for illustration here
myStopping <- StoppingMinPatients(nPatients = 10) | StoppingMissingDose()

## Specified the design
design <- DualResponsesSamplesDesign(
  nextBest = mynextbest,
  cohort_size = mySize,
  startingDose = 25,
  model = DLEmodel,
  eff_model = Effmodel,
  data = data,
  stopping = myStopping,
  increments = myIncrements
)
## specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499)

myTruthEff <- c(
  -0.5478867,
  0.1645417,
  0.5248031,
  0.7604467,
  0.9333009,
  1.0687031,
  1.1793942,
  1.2726408,
  1.3529598,
  1.4233411,
  1.4858613,
  1.5420182
)


## specify the options for MCMC
# for illustration purposes, in reality larger number of burnin and samples shoud be used
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
## The simulation
## For illustration purpose only 1 simulation is produced (nsim=1).
mySim <- simulate(
  object = design,
  args = NULL,
  trueDLE = myTruthDLE,
  trueEff = myTruthEff,
  trueSigma2 = 0.025,
  trueSigma2betaW = 1,
  nsim = 1,
  seed = 819,
  parallel = FALSE,
  mcmcOptions = options
)
## summarize the simulation results
summary(mySim, trueDLE = myTruthDLE, trueEff = myTruthEff)

# nolint end
# nolint start

## If DLE and efficacy responses are considered in the simulations and the 'EffFlexi' class is used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid = seq(25, 300, 25))
## First for the DLE model
## The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## for the efficacy model
Effmodel <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)


## specified the next best
mynextbest <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

## The increments (see Increments class examples)
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)
## cohort size of 3
mySize <- CohortSizeConst(size = 3)
## Stop only when 10 subjects are treated:
## very low sample size is just for illustration here
myStopping <- StoppingMinPatients(nPatients = 10) | StoppingMissingDose()

## Specified the design
design <- DualResponsesSamplesDesign(
  nextBest = mynextbest,
  cohort_size = mySize,
  startingDose = 25,
  model = DLEmodel,
  eff_model = Effmodel,
  data = data,
  stopping = myStopping,
  increments = myIncrements
)
## specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499)

myTruthEff <- c(
  -0.5478867,
  0.1645417,
  0.5248031,
  0.7604467,
  0.9333009,
  1.0687031,
  1.1793942,
  1.2726408,
  1.3529598,
  1.4233411,
  1.4858613,
  1.5420182
)


## specify the options for MCMC
# for illustration purposes, in reality larger number of burnin and samples shoud be used
options <- McmcOptions(burnin = 5, step = 1, samples = 10)
## The simulation
## For illustration purpose only 1 simulation is produced (nsim=1).
mySim <- simulate(
  object = design,
  args = NULL,
  trueDLE = myTruthDLE,
  trueEff = myTruthEff,
  trueSigma2 = 0.025,
  trueSigma2betaW = 1,
  nsim = 1,
  seed = 819,
  parallel = FALSE,
  mcmcOptions = options
)
## summarize the simulation results
summary(mySim, trueDLE = myTruthDLE, trueEff = myTruthEff)

# nolint end

Summarize `PseudoDualSimulations`

Description

Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model).

Usage

## S4 method for signature 'PseudoDualSimulations'
summary(
  object,
  trueDLE,
  trueEff,
  targetEndOfTrial = 0.3,
  targetDuringTrial = 0.35,
  ...
)
## S4 method for signature 'PseudoDualSimulations'
summary(
  object,
  trueDLE,
  trueEff,
  targetEndOfTrial = 0.3,
  targetDuringTrial = 0.35,
  ...
)

Arguments

object

(PseudoDualSimulations)
the object we want to summarize.

trueDLE

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) of DLE.

trueEff

(function)
a function which takes as input a dose (vector) and returns the mean efficacy value(s) (vector).

targetEndOfTrial

(number)
the target probability of DLE that are used at the end of a trial. Default at 0.3.

targetDuringTrial

(number)
the target probability of DLE that are used during the trial. Default at 0.35.

...

additional arguments can be supplied here for trueDLE and trueEff.

Value

An object of class PseudoDualSimulationsSummary.

Examples

# Obtain the plot for the simulation results if DLE and efficacy responses
# are considered in the simulations.

# Specified simulations when no samples are used.
emptydata <- DataDual(doseGrid = seq(25, 300, 25))

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model of 'ModelEff' (e.g 'Effloglog') class.
eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGain' class.
my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Cohort size of 3.
my_size <- CohortSizeConst(size = 3)

# Stop when 36 subjects are treated or next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design. (For details please refer to the 'DualResponsesDesign' example.)
my_design <- DualResponsesDesign(
  nextBest = my_next_best,
  model = dle_model,
  eff_model = eff_model,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the true DLE and efficacy curves.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)
my_truth_eff <- efficacyFunction(eff_model, theta1 = -4.818429, theta2 = 3.653058)


# Specify the simulations and generate the 2 trials.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 2,
  seed = 819,
  parallel = FALSE
)

# Produce a summary of the simulations.
summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)


# Example where DLE and efficacy samples are involved.
# Please refer to design-method 'simulate DualResponsesSamplesDesign' examples for details.

# Specify the next best rule.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# Specify the design.
my_design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)

# For illustration purpose 50 burn-ins to generate 200 samples are used.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)

# fmt: skip

# For illustration purpose 2 simulation are created.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 2,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)

# Produce a summary of the simulations.
summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

# Obtain the plot for the simulation results if DLE and efficacy responses
# are considered in the simulations.

# Specified simulations when no samples are used.
emptydata <- DataDual(doseGrid = seq(25, 300, 25))

# The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class.
dle_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The efficacy model of 'ModelEff' (e.g 'Effloglog') class.
eff_model <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = emptydata
)

# The escalation rule using the 'NextBestMaxGain' class.
my_next_best <- NextBestMaxGain(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Cohort size of 3.
my_size <- CohortSizeConst(size = 3)

# Stop when 36 subjects are treated or next dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design. (For details please refer to the 'DualResponsesDesign' example.)
my_design <- DualResponsesDesign(
  nextBest = my_next_best,
  model = dle_model,
  eff_model = eff_model,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the true DLE and efficacy curves.
my_truth_dle <- probFunction(dle_model, phi1 = -53.66584, phi2 = 10.50499)
my_truth_eff <- efficacyFunction(eff_model, theta1 = -4.818429, theta2 = 3.653058)


# Specify the simulations and generate the 2 trials.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 2,
  seed = 819,
  parallel = FALSE
)

# Produce a summary of the simulations.
summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)


# Example where DLE and efficacy samples are involved.
# Please refer to design-method 'simulate DualResponsesSamplesDesign' examples for details.

# Specify the next best rule.
my_next_best <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

# Specify the design.
my_design <- DualResponsesSamplesDesign(
  nextBest = my_next_best,
  cohort_size = my_size,
  startingDose = 25,
  model = dle_model,
  eff_model = eff_model,
  data = emptydata,
  stopping = my_stopping,
  increments = my_increments
)

# For illustration purpose 50 burn-ins to generate 200 samples are used.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)

# fmt: skip

# For illustration purpose 2 simulation are created.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff,
  trueNu = 1 / 0.025,
  nsim = 2,
  mcmcOptions = my_options,
  seed = 819,
  parallel = FALSE
)

# Produce a summary of the simulations.
summary(
  my_sim,
  trueDLE = my_truth_dle,
  trueEff = my_truth_eff
)

Summarize `PseudoSimulations`

Description

Summarize the simulations, relative to a given truth.

Usage

## S4 method for signature 'PseudoSimulations'
summary(object, truth, targetEndOfTrial = 0.3, targetDuringTrial = 0.35, ...)
## S4 method for signature 'PseudoSimulations'
summary(object, truth, targetEndOfTrial = 0.3, targetDuringTrial = 0.35, ...)

Arguments

object

(PseudoSimulations)
the object we want to summarize.

truth

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity.

targetEndOfTrial

(number)
the target probability of DLE wanted to achieve at the end of a trial.

targetDuringTrial

(number)
the target probability of DLE wanted to achieve during a trial.

...

additional arguments can be supplied here for truth.

Value

An object of class PseudoSimulationsSummary.

Examples

emptydata <- Data(doseGrid = seq(25, 300, 25))

# The design incorporate DLE responses and DLE samples.
# Specify the model of 'ModelTox' class eg 'LogisticIndepBeta' class model.
my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The escalation rule.
td_next_best <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# Cohort size is 3 subjects.
my_size <- CohortSizeConst(size = 3)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Stopp when the maximum sample size of 36 patients has been reached or the next
# dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design. (For details please refer to the 'TDDesign' example.)
my_design <- TDDesign(
  model = my_model,
  nextBest = td_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the truth of the DLE responses.
my_truth <- probFunction(my_model, phi1 = -53.66584, phi2 = 10.50499)

# For illustration purpose 50 burn-ins to generate 200 samples are used.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)

# Refer to design-method 'simulate TDDesign' examples for details.
# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  truth = my_truth,
  nsim = 1,
  seed = 819,
  parallel = FALSE,
  mcmcOptions = my_options
)

# Produce a summary of the simulations.
summary(
  my_sim,
  truth = my_truth
)

# Example where DLE samples are involved.

# Specify the next best rule.
td_next_best <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

# The design.
my_design <- TDsamplesDesign(
  model = my_model,
  nextBest = td_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# For illustration purpose 50 burn-ins to generate 200 samples are used.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)

# For illustration purpose 2 trials are simulated.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  truth = my_truth,
  nsim = 2,
  seed = 819,
  mcmcOptions = my_options,
  parallel = FALSE
)

# Produce a summary of the simulations.
summary(
  my_sim,
  truth = my_truth
)
emptydata <- Data(doseGrid = seq(25, 300, 25))

# The design incorporate DLE responses and DLE samples.
# Specify the model of 'ModelTox' class eg 'LogisticIndepBeta' class model.
my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = emptydata
)

# The escalation rule.
td_next_best <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

# Cohort size is 3 subjects.
my_size <- CohortSizeConst(size = 3)

# Allow increase of 200%.
my_increments <- IncrementsRelative(intervals = 0, increments = 2)

# Stopp when the maximum sample size of 36 patients has been reached or the next
# dose is NA.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# Specify the design. (For details please refer to the 'TDDesign' example.)
my_design <- TDDesign(
  model = my_model,
  nextBest = td_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# Specify the truth of the DLE responses.
my_truth <- probFunction(my_model, phi1 = -53.66584, phi2 = 10.50499)

# For illustration purpose 50 burn-ins to generate 200 samples are used.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)

# Refer to design-method 'simulate TDDesign' examples for details.
# For illustration purpose only 1 simulation is produced.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  truth = my_truth,
  nsim = 1,
  seed = 819,
  parallel = FALSE,
  mcmcOptions = my_options
)

# Produce a summary of the simulations.
summary(
  my_sim,
  truth = my_truth
)

# Example where DLE samples are involved.

# Specify the next best rule.
td_next_best <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

# The design.
my_design <- TDsamplesDesign(
  model = my_model,
  nextBest = td_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = emptydata,
  startingDose = 25
)

# For illustration purpose 50 burn-ins to generate 200 samples are used.
my_options <- McmcOptions(burnin = 50, step = 2, samples = 200)

# For illustration purpose 2 trials are simulated.
my_sim <- simulate(
  object = my_design,
  args = NULL,
  truth = my_truth,
  nsim = 2,
  seed = 819,
  mcmcOptions = my_options,
  parallel = FALSE
)

# Produce a summary of the simulations.
summary(
  my_sim,
  truth = my_truth
)

Summarize Model-Based Design Simulations

Description

Summarize the model-based design simulations, relative to a given truth.

Usage

## S4 method for signature 'Simulations'
summary(object, truth, target = c(0.2, 0.35), ...)
## S4 method for signature 'Simulations'
summary(object, truth, target = c(0.2, 0.35), ...)

Arguments

object

(Simulations)
the object we want to summarize.

truth

(function)
a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity.

target

(numeric)
the target toxicity interval (default: 20-35%) used for the computations.

...

additional arguments can be supplied here for truth.

Value

An object of class SimulationsSummary.

Examples

# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3 | StoppingMissingDose()

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design
design <- Design(
  model = model,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = emptydata,
  startingDose = 3
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 1000
)
time <- system.time(
  mySims <- simulate(
    design,
    args = NULL,
    truth = myTruth,
    nsim = 1,
    seed = 819,
    mcmcOptions = options,
    parallel = FALSE,
    derive = list(
      max_mtd = max,
      mean_mtd = mean,
      median_mtd = median
    ),
  )
)[3]

# Summarize the Results of the Simulations
summary(mySims, truth = myTruth)

# nolint end
# nolint start

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model
model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size
mySize1 <- CohortSizeRange(
  intervals = c(0, 30),
  cohort_size = c(1, 3)
)
mySize2 <- CohortSizeDLT(
  intervals = c(0, 1),
  cohort_size = c(1, 3)
)
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts = 3)
myStopping2 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping3 <- StoppingMinPatients(nPatients = 20)
myStopping <- (myStopping1 & myStopping2) | myStopping3 | StoppingMissingDose()

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

# Initialize the design
design <- Design(
  model = model,
  nextBest = myNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = emptydata,
  startingDose = 3
)

## define the true function
myTruth <- probFunction(model, alpha0 = 7, alpha1 = 8)

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study
# this should be increased of course
options <- McmcOptions(
  burnin = 100,
  step = 2,
  samples = 1000
)
time <- system.time(
  mySims <- simulate(
    design,
    args = NULL,
    truth = myTruth,
    nsim = 1,
    seed = 819,
    mcmcOptions = options,
    parallel = FALSE,
    derive = list(
      max_mtd = max,
      mean_mtd = mean,
      median_mtd = median
    ),
  )
)[3]

# Summarize the Results of the Simulations
summary(mySims, truth = myTruth)

# nolint end

`TDDesign`

Description

TDDesign is the class of design based only on DLT responses using ModelTox class model (i.e. LogisticIndepBeta) without MCMC samples.

Usage

TDDesign(
  model,
  stopping,
  increments,
  pl_cohort_size = CohortSizeConst(0L),
  ...
)

.DefaultTDDesign()
TDDesign(
  model,
  stopping,
  increments,
  pl_cohort_size = CohortSizeConst(0L),
  ...
)

.DefaultTDDesign()

Arguments

model

(ModelTox)
see slot definition.

stopping

(Stopping)
see slot definition.

increments

(Increments)
see slot definition.

pl_cohort_size

(CohortSize)
see slot definition.

...

Arguments passed on to RuleDesign

nextBest: (NextBest)
see slot definition.
cohort_size: (CohortSize)
see slot definition.
data: (Data)
see slot definition.
startingDose: (number)
see slot definition.

Slots

model: (ModelTox)
the pseudo DLT model to be used.
stopping: (Stopping)
stopping rule(s) for the trial.
increments: (Increments)
how to control increments between dose levels.
pl_cohort_size: (CohortSize)
rules for the cohort sizes for placebo, if any planned (defaults to constant 0 placebo patients).

Note

Typically, end users will not use the .DefaultTDDesign() function.

Examples

empty_data <- Data(doseGrid = seq(25, 300, 25))

my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)

# The escalation rule.
my_next_best <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

my_size <- CohortSizeConst(size = 3)

# The increments for the dose-escalation process:
#  the maximum increase of 200% for doses up to the maximum dose in grid,
#  the maximum increase of 200% for dose above the maximum dose in grid.
my_increments <- IncrementsRelative(
  intervals = range(empty_data@doseGrid),
  increments = c(2, 2)
)

# Stop when the maximum sample size of 36 patients is reached.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# The design with all the above information and starting with a dose of 25.
# This design incorporates only DLT responses and no DLT samples are involved
# during the simulation.
design <- TDDesign(
  model = my_model,
  stopping = my_stopping,
  increments = my_increments,
  nextBest = my_next_best,
  cohort_size = my_size,
  data = empty_data,
  startingDose = 25
)
empty_data <- Data(doseGrid = seq(25, 300, 25))

my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)

# The escalation rule.
my_next_best <- NextBestTD(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3
)

my_size <- CohortSizeConst(size = 3)

# The increments for the dose-escalation process:
#  the maximum increase of 200% for doses up to the maximum dose in grid,
#  the maximum increase of 200% for dose above the maximum dose in grid.
my_increments <- IncrementsRelative(
  intervals = range(empty_data@doseGrid),
  increments = c(2, 2)
)

# Stop when the maximum sample size of 36 patients is reached.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# The design with all the above information and starting with a dose of 25.
# This design incorporates only DLT responses and no DLT samples are involved
# during the simulation.
design <- TDDesign(
  model = my_model,
  stopping = my_stopping,
  increments = my_increments,
  nextBest = my_next_best,
  cohort_size = my_size,
  data = empty_data,
  startingDose = 25
)

`TDsamplesDesign`

Description

TDsamplesDesign is the class of design based only on DLT responses using ModelTox class model (i.e. LogisticIndepBeta) as well as MCMC samples obtained for this model.

Usage

TDsamplesDesign(
  model,
  stopping,
  increments,
  pl_cohort_size = CohortSizeConst(0L),
  ...
)

.DefaultTDsamplesDesign()
TDsamplesDesign(
  model,
  stopping,
  increments,
  pl_cohort_size = CohortSizeConst(0L),
  ...
)

.DefaultTDsamplesDesign()

Arguments

model

(ModelTox)
see slot definition.

stopping

(Stopping)
see slot definition.

increments

(Increments)
see slot definition.

pl_cohort_size

(CohortSize)
see slot definition.

...

Arguments passed on to RuleDesign

nextBest: (NextBest)
see slot definition.
cohort_size: (CohortSize)
see slot definition.
data: (Data)
see slot definition.
startingDose: (number)
see slot definition.

Slots

model: (ModelTox)
the pseudo DLT model to be used.
stopping: (Stopping)
stopping rule(s) for the trial.
increments: (Increments)
how to control increments between dose levels.
pl_cohort_size: (CohortSize)
rules for the cohort sizes for placebo, if any planned (defaults to constant 0 placebo patients).

Note

Typically, end users will not use the .DefaultTDsamplesDesign() function.

Examples

empty_data <- Data(doseGrid = seq(25, 300, 25))

my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)

# The escalation rule.
my_next_best <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

my_size <- CohortSizeConst(size = 3)

# The increments for the dose-escalation process:
#  the maximum increase of 200% for doses up to the maximum dose in grid,
#  the maximum increase of 200% for dose above the maximum dose in grid.
my_increments <- IncrementsRelative(
  intervals = range(empty_data@doseGrid),
  increments = c(2, 2)
)

# Stop when the maximum sample size of 36 patients is reached.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# The design with all the above information and starting with a dose of 25.
design <- TDsamplesDesign(
  model = my_model,
  stopping = my_stopping,
  increments = my_increments,
  nextBest = my_next_best,
  cohort_size = my_size,
  data = empty_data,
  startingDose = 25
)
empty_data <- Data(doseGrid = seq(25, 300, 25))

my_model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)

# The escalation rule.
my_next_best <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

my_size <- CohortSizeConst(size = 3)

# The increments for the dose-escalation process:
#  the maximum increase of 200% for doses up to the maximum dose in grid,
#  the maximum increase of 200% for dose above the maximum dose in grid.
my_increments <- IncrementsRelative(
  intervals = range(empty_data@doseGrid),
  increments = c(2, 2)
)

# Stop when the maximum sample size of 36 patients is reached.
my_stopping <- StoppingMinPatients(nPatients = 36) | StoppingMissingDose()

# The design with all the above information and starting with a dose of 25.
design <- TDsamplesDesign(
  model = my_model,
  stopping = my_stopping,
  increments = my_increments,
  nextBest = my_next_best,
  cohort_size = my_size,
  data = empty_data,
  startingDose = 25
)

Tidying `CrmPackClass` objects

Description

In the spirit of the broom package, provide a method to convert a CrmPackClass object to a (list of) tibbles.

Following the principles of the broom package, convert a CrmPackClass object to a (list of) tibbles. This is a basic, default representation.

A method that tidies a GeneralData object.

A method that tidies a Data object.

A method that tidies a DataGrouped object.

A method that tidies a DataDA object.

A method that tidies a DataDual object.

A method that tidies a DataParts object.

A method that tidies a DataMixture object.

A method that tidies a DataOrdinal object.

A method that tidies a LogisticIndepBeta object.

A method that tidies a Effloglog object.

Usage

tidy(x, ...)

## S4 method for signature 'CrmPackClass'
tidy(x, ...)

## S4 method for signature 'GeneralData'
tidy(x, ...)

## S4 method for signature 'Data'
tidy(x, ...)

## S4 method for signature 'DataGrouped'
tidy(x, ...)

## S4 method for signature 'DataDA'
tidy(x, ...)

## S4 method for signature 'DataDual'
tidy(x, ...)

## S4 method for signature 'DataParts'
tidy(x, ...)

## S4 method for signature 'DataMixture'
tidy(x, ...)

## S4 method for signature 'DataOrdinal'
tidy(x, ...)

## S4 method for signature 'Simulations'
tidy(x, ...)

## S4 method for signature 'LogisticIndepBeta'
tidy(x, ...)

## S4 method for signature 'Effloglog'
tidy(x, ...)

## S4 method for signature 'IncrementsMaxToxProb'
tidy(x, ...)

## S4 method for signature 'IncrementsRelative'
tidy(x, ...)

## S4 method for signature 'CohortSizeDLT'
tidy(x, ...)

## S4 method for signature 'CohortSizeMin'
tidy(x, ...)

## S4 method for signature 'CohortSizeMax'
tidy(x, ...)

## S4 method for signature 'CohortSizeRange'
tidy(x, ...)

## S4 method for signature 'CohortSizeParts'
tidy(x, ...)

## S4 method for signature 'IncrementsMin'
tidy(x, ...)

## S4 method for signature 'IncrementsRelative'
tidy(x, ...)

## S4 method for signature 'IncrementsRelativeDLT'
tidy(x, ...)

## S4 method for signature 'IncrementsRelativeParts'
tidy(x, ...)

## S4 method for signature 'NextBestNCRM'
tidy(x, ...)

## S4 method for signature 'NextBestNCRMLoss'
tidy(x, ...)

## S4 method for signature 'DualDesign'
tidy(x, ...)

## S4 method for signature 'Samples'
tidy(x, ...)
tidy(x, ...)

## S4 method for signature 'CrmPackClass'
tidy(x, ...)

## S4 method for signature 'GeneralData'
tidy(x, ...)

## S4 method for signature 'Data'
tidy(x, ...)

## S4 method for signature 'DataGrouped'
tidy(x, ...)

## S4 method for signature 'DataDA'
tidy(x, ...)

## S4 method for signature 'DataDual'
tidy(x, ...)

## S4 method for signature 'DataParts'
tidy(x, ...)

## S4 method for signature 'DataMixture'
tidy(x, ...)

## S4 method for signature 'DataOrdinal'
tidy(x, ...)

## S4 method for signature 'Simulations'
tidy(x, ...)

## S4 method for signature 'LogisticIndepBeta'
tidy(x, ...)

## S4 method for signature 'Effloglog'
tidy(x, ...)

## S4 method for signature 'IncrementsMaxToxProb'
tidy(x, ...)

## S4 method for signature 'IncrementsRelative'
tidy(x, ...)

## S4 method for signature 'CohortSizeDLT'
tidy(x, ...)

## S4 method for signature 'CohortSizeMin'
tidy(x, ...)

## S4 method for signature 'CohortSizeMax'
tidy(x, ...)

## S4 method for signature 'CohortSizeRange'
tidy(x, ...)

## S4 method for signature 'CohortSizeParts'
tidy(x, ...)

## S4 method for signature 'IncrementsMin'
tidy(x, ...)

## S4 method for signature 'IncrementsRelative'
tidy(x, ...)

## S4 method for signature 'IncrementsRelativeDLT'
tidy(x, ...)

## S4 method for signature 'IncrementsRelativeParts'
tidy(x, ...)

## S4 method for signature 'NextBestNCRM'
tidy(x, ...)

## S4 method for signature 'NextBestNCRMLoss'
tidy(x, ...)

## S4 method for signature 'DualDesign'
tidy(x, ...)

## S4 method for signature 'Samples'
tidy(x, ...)

Arguments

x

(CrmPackClass)
the object to be tidied.

...

potentially used by class-specific methods.

Value

A (list of) tibble(s) representing the object in tidy form.

The tibble::tibble object.

The list of tibble::tibble objects.

Usage Notes

The prior observations are indicated by a Cohort value of 0 in the returned tibble.

Examples

CohortSizeConst(3) %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
# Create a sample Data object
sample_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(1, 2, 3, 4, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  response = c(0, 0, 0, 0, 0, 1, NA, NA),
  backfilled = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE)
)

# Tidy the Data object
tidied_data <- tidy(sample_data)

# Print the tidied data
print(tidied_data)
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultSimulations() %>% tidy()
.DefaultLogisticIndepBeta() %>% tidy()
.DefaultEffloglog() %>% tidy()
IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "CRS" = 0.05)) %>% tidy()
CohortSizeRange(intervals = c(0, 20), cohort_size = c(1, 3)) %>% tidy()
.DefaultCohortSizeDLT() %>% tidy()
.DefaultCohortSizeMin() %>% tidy()
.DefaultCohortSizeMax() %>% tidy()
.DefaultCohortSizeRange() %>% tidy()
CohortSizeParts(cohort_sizes = c(1, 3)) %>% tidy()
.DefaultIncrementsMin() %>% tidy()
CohortSizeRange(intervals = c(0, 20), cohort_size = c(1, 3)) %>% tidy()
x <- .DefaultIncrementsRelativeDLT()
x %>% tidy()
.DefaultIncrementsRelativeParts() %>% tidy()
NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
) %>%
  tidy()
.DefaultNextBestNCRMLoss() %>% tidy()
.DefaultDualDesign() %>% tidy()
options <- McmcOptions(
  burnin = 100,
  step = 1,
  samples = 2000
)

emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

samples <- mcmc(emptydata, model, options)
samples %>% tidy()
CohortSizeConst(3) %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
# Create a sample Data object
sample_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  cohort = c(1, 2, 3, 4, 5, 6, 6, 6),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  response = c(0, 0, 0, 0, 0, 1, NA, NA),
  backfilled = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE)
)

# Tidy the Data object
tidied_data <- tidy(sample_data)

# Print the tidied data
print(tidied_data)
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultData() %>% tidy()
.DefaultDataOrdinal() %>% tidy()
.DefaultDataGrouped() %>% tidy()
.DefaultDataDA() %>% tidy()
.DefaultSimulations() %>% tidy()
.DefaultLogisticIndepBeta() %>% tidy()
.DefaultEffloglog() %>% tidy()
IncrementsMaxToxProb(prob = c("DLAE" = 0.2, "CRS" = 0.05)) %>% tidy()
CohortSizeRange(intervals = c(0, 20), cohort_size = c(1, 3)) %>% tidy()
.DefaultCohortSizeDLT() %>% tidy()
.DefaultCohortSizeMin() %>% tidy()
.DefaultCohortSizeMax() %>% tidy()
.DefaultCohortSizeRange() %>% tidy()
CohortSizeParts(cohort_sizes = c(1, 3)) %>% tidy()
.DefaultIncrementsMin() %>% tidy()
CohortSizeRange(intervals = c(0, 20), cohort_size = c(1, 3)) %>% tidy()
x <- .DefaultIncrementsRelativeDLT()
x %>% tidy()
.DefaultIncrementsRelativeParts() %>% tidy()
NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
) %>%
  tidy()
.DefaultNextBestNCRMLoss() %>% tidy()
.DefaultDualDesign() %>% tidy()
options <- McmcOptions(
  burnin = 100,
  step = 1,
  samples = 2000
)

emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

samples <- mcmc(emptydata, model, options)
samples %>% tidy()

`TITELogisticLogNormal`

Description

TITELogisticLogNormal is the class for TITE-CRM based on a logistic regression model using a bivariate normal prior on the intercept and log slope parameters.

This class inherits from the LogisticLogNormal.

Usage

TITELogisticLogNormal(weight_method = "linear", ...)

.DefaultTITELogisticLogNormal()
TITELogisticLogNormal(weight_method = "linear", ...)

.DefaultTITELogisticLogNormal()

Arguments

weight_method

(string)
see the slot description.

...

Arguments passed on to LogisticLogNormal

mean: (numeric)
the prior mean vector.
cov: (matrix)
the prior covariance matrix. The precision matrix prec is internally calculated as an inverse of cov.
ref_dose: (number)
the reference dose $x*$ (strictly positive number).

Details

Basically, the adaptive function allocates more weight to each record than the linear function when DLTs are observed early and less weight when DLTs are observed late. When DLT times are evenly distributed both weights are similar. In addition, with more DLTs, the adaptive weights become more extreme and different from the linear weights.

Slots

weight_method: (string)
the weight function method: either linear or adaptive; see Liu et al. (2013).

Note

Typically, end users will not use the .DefaultTITELogisticLogNormal() function.

References

Liu S, Yin G, Yuan Y (2013). “Bayesian data augmentation dose finding with continual reassessment method and delayed toxicity.” The Annals of Applied Statistics, 7(4), 2138–2156. doi:10.1214/13-AOAS661.

Examples

my_model <- TITELogisticLogNormal(
  mean = c(0, 1),
  cov = diag(2),
  ref_dose = 1,
  weight_method = "linear"
)

my_model1 <- TITELogisticLogNormal(
  mean = c(0, 1),
  cov = diag(2),
  ref_dose = 1,
  weight_method = "adaptive"
)
my_model <- TITELogisticLogNormal(
  mean = c(0, 1),
  cov = diag(2),
  ref_dose = 1,
  weight_method = "linear"
)

my_model1 <- TITELogisticLogNormal(
  mean = c(0, 1),
  cov = diag(2),
  ref_dose = 1,
  weight_method = "adaptive"
)

Updating `Data` Objects

Description

A method that updates existing Data object with new data.

Usage

## S4 method for signature 'Data'
update(
  object,
  x,
  y,
  response = rep(NA_integer_, length(y)),
  ID = length(object@ID) + seq_along(y),
  new_cohort = TRUE,
  check = TRUE,
  backfill = FALSE,
  cohort = NULL,
  ...
)
## S4 method for signature 'Data'
update(
  object,
  x,
  y,
  response = rep(NA_integer_, length(y)),
  ID = length(object@ID) + seq_along(y),
  new_cohort = TRUE,
  check = TRUE,
  backfill = FALSE,
  cohort = NULL,
  ...
)

Arguments

object

(Data)
object you want to update.

x

(number)
the dose level (one level only!).

y

(integer)
the DLT vector (0/1 vector) for all patients in this cohort. You can also supply numeric vectors, but these will then be converted to integer internally.

response

(integer)
the efficacy response vector (0/1 vector). May contain NA.

ID

(integer)
the patient IDs. You can also supply numeric vectors, but these will then be converted to integer internally.

new_cohort

(flag)
if TRUE (default) the new data are assigned to a new cohort.

check

(flag)
whether the validation of the updated object should be conducted. See details below.

backfill

(flag)
whether the new patients being added are from a backfill cohort.

cohort

(int)
if provided, the new patients will be assigned to this cohort index. If NULL (default), the cohort index will be determined based on the new_cohort parameter.

...

not used.

Details

The current implementation of this update method allows for updating the Data class object by adding a single dose level x only. However, there might be some use cases where the new cohort to be added contains a placebo and active dose. Hence, such update would need to be performed iteratively by calling the update method twice. For example, in the first call a user can add a placebo, and then in the second call, an active dose. Since having a cohort with placebo only is not allowed, the update method would normally throw the error when attempting to add a placebo in the first call. To allow for such updates, the check parameter should be then set to FALSE for that first call.

Value

The new, updated Data object.

Examples

# Create some data of class 'Data'.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Update the data with a new cohort.
my_data1 <- update(my_data, x = 20, y = c(0L, 1L, 1L))
my_data1
# Create some data of class 'Data'.
my_data <- Data(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Update the data with a new cohort.
my_data1 <- update(my_data, x = 20, y = c(0L, 1L, 1L))
my_data1

Updating `DataDA` Objects

Description

A method that updates existing DataDA object with new data.

Usage

## S4 method for signature 'DataDA'
update(object, u, t0, trialtime, y, ..., check = TRUE)
## S4 method for signature 'DataDA'
update(object, u, t0, trialtime, y, ..., check = TRUE)

Arguments

object

(DataDA)
object you want to update.

u

(numeric)
the new DLT free survival times for all patients, i.e. for existing patients in the object as well as for new patients.

t0

(numeric)
the time that each patient starts DLT observation window. This parameter covers all patients, i.e. existing patients in the object as well as for new patients.

trialtime

(number)
current time in the trial, i.e. a followup time.

y

(numeric)
the new DLTs for all patients, i.e. for existing patients in the object as well as for new patients.

...

further arguments passed to Data update method update-Data. These are used when there are new patients to be added to the cohort.

check

(flag)
whether the validation of the updated object should be conducted. See help for update-Data for more details on the use case of this parameter.

Value

The new, updated DataDA object.

Note

This function is capable of not only adding new patients but also updates existing ones with respect to y, t0, u slots.

Examples

# Create an object of class 'DataDA'.
my_data <- DataDA(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60
)

# Update the data.
my_data1 <- update(
  object = my_data,
  y = c(my_data@y, 0), # The 'y' will be updated according to 'u'.
  u = c(my_data@u, 20),
  t0 = c(my_data@t0, 95),
  x = 20,
  trialtime = 120 # This is the global timeline for a trial.
)
my_data1
# Create an object of class 'DataDA'.
my_data <- DataDA(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60
)

# Update the data.
my_data1 <- update(
  object = my_data,
  y = c(my_data@y, 0), # The 'y' will be updated according to 'u'.
  u = c(my_data@u, 20),
  t0 = c(my_data@t0, 95),
  x = 20,
  trialtime = 120 # This is the global timeline for a trial.
)
my_data1

Updating `DataDual` Objects

Description

A method that updates existing DataDual object with new data.

Usage

## S4 method for signature 'DataDual'
update(object, w, ..., check = TRUE)
## S4 method for signature 'DataDual'
update(object, w, ..., check = TRUE)

Arguments

object

(DataDual)
object you want to update.

w

(numeric)
the continuous vector of biomarker values for all the patients in this update.

...

further arguments passed to Data update method update-Data.

check

(flag)
whether the validation of the updated object should be conducted. See help for update-Data for more details on the use case of this parameter.

Value

The new, updated DataDual object.

Examples

# Create some data of class 'DataDual'.
my_data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  w = rnorm(8),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Update the data with a new cohort.
my_data1 <- update(my_data, x = 20, y = c(0, 1, 1), w = c(0.4, 1.2, 2.2))
my_data1
# Create some data of class 'DataDual'.
my_data <- DataDual(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 0, 0, 0, 0, 1, 0),
  w = rnorm(8),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2))
)

# Update the data with a new cohort.
my_data1 <- update(my_data, x = 20, y = c(0, 1, 1), w = c(0.4, 1.2, 2.2))
my_data1

Updating `DataOrdinal` Objects

Description

A method that updates existing DataOrdinal object with new data.

Usage

## S4 method for signature 'DataOrdinal'
update(
  object,
  x,
  y,
  ID = length(object@ID) + seq_along(y),
  new_cohort = TRUE,
  check = TRUE,
  ...
)
## S4 method for signature 'DataOrdinal'
update(
  object,
  x,
  y,
  ID = length(object@ID) + seq_along(y),
  new_cohort = TRUE,
  check = TRUE,
  ...
)

Arguments

object

(DataOrdinal)
object you want to update.

x

(number)
the dose level (one level only!).

y

(integer)
the vector of toxicity grades (0, 1, 2, ...) for all patients in this cohort. You can also supply numeric vectors, but these will then be converted to integer internally.

ID

(integer)
the patient IDs. You can also supply numeric vectors, but these will then be converted to integer internally.

new_cohort

(flag)
if TRUE (default) the new data are assigned to a new cohort.

check

(flag)
whether the validation of the updated object should be conducted. See Details below.

...

not used.

Details

The current implementation of this update method allows for updating the DataOrdinal class object by adding a single dose level x only. However, there might be some use cases where the new cohort to be added contains a placebo and active dose. Hence, such update would need to be performed iteratively by calling the update method twice. For example, in the first call a user can add a placebo, and then in the second call, an active dose. Since having a cohort with placebo only is not allowed, the update method would normally throw the error when attempting to add a placebo in the first call. To allow for such updates, the check parameter should be then set to FALSE for that first call.

Value

The new, updated DataOrdinal object.

Examples

data <- DataOrdinal(
  x = c(10, 20, 30, 40, 50, 50, 50, 60, 60, 60),
  y = as.integer(c(0, 0, 0, 0, 0, 1, 0, 0, 1, 2)),
  ID = 1L:10L,
  cohort = as.integer(c(1:4, 5, 5, 5, 6, 6, 6)),
  doseGrid = c(seq(from = 10, to = 100, by = 10)),
  yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L),
  placebo = FALSE
)

update(data, x = 70, y = c(1L, 2L, 1L))
data <- DataOrdinal(
  x = c(10, 20, 30, 40, 50, 50, 50, 60, 60, 60),
  y = as.integer(c(0, 0, 0, 0, 0, 1, 0, 0, 1, 2)),
  ID = 1L:10L,
  cohort = as.integer(c(1:4, 5, 5, 5, 6, 6, 6)),
  doseGrid = c(seq(from = 10, to = 100, by = 10)),
  yCategories = c("No tox" = 0L, "Sub-tox AE" = 1L, "DLT" = 2L),
  placebo = FALSE
)

update(data, x = 70, y = c(1L, 2L, 1L))

Updating `DataParts` Objects

Description

A method that updates existing DataParts object with new data.

Usage

## S4 method for signature 'DataParts'
update(object, x, y, ..., check = TRUE)
## S4 method for signature 'DataParts'
update(object, x, y, ..., check = TRUE)

Arguments

object

(DataParts)
object you want to update.

x

(number)
the dose level (one level only!).

y

(integer)
the DLT vector (0/1 vector) for all patients in this cohort. You can also supply numeric vectors, but these will then be converted to integer internally.

...

further arguments passed to Data update method update-Data.

check

(flag)
whether the validation of the updated object should be conducted. See help for update-Data for more details on the use case of this parameter.

Value

The new, updated DataParts object.

Examples

# Create an object of class 'DataParts'.
my_data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)

# Update the data with a new cohort.
# Note that since we reached the last level from 'part1Ladder'
# then the 'nextPart' is switched from '1' to '2'.
my_data1 <- update(my_data, x = 10, y = 0L)
my_data1
# Create an object of class 'DataParts'.
my_data <- DataParts(
  x = c(0.1, 0.5, 1.5),
  y = c(0, 0, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  part = c(1L, 1L, 1L),
  nextPart = 1L,
  part1Ladder = c(0.1, 0.5, 1.5, 3, 6, 10)
)

# Update the data with a new cohort.
# Note that since we reached the last level from 'part1Ladder'
# then the 'nextPart' is switched from '1' to '2'.
my_data1 <- update(my_data, x = 10, y = 0L)
my_data1

Update method for the `ModelPseudo` model class. This is a method to update the model class slots (estimates, parameters, variables and etc.), when the new data (e.g. new observations of responses) are available. This method is mostly used to obtain new modal estimates for pseudo model parameters.

Description

Update method for the ModelPseudo model class. This is a method to update the model class slots (estimates, parameters, variables and etc.), when the new data (e.g. new observations of responses) are available. This method is mostly used to obtain new modal estimates for pseudo model parameters.

Usage

## S4 method for signature 'ModelPseudo'
update(object, data, ...)
## S4 method for signature 'ModelPseudo'
update(object, data, ...)

Arguments

object

(ModelPseudo)
the model to update.

data

(Data)
all currently available of data.

...

not used.

Value

the new ModelPseudo class object.

Examples

# Update the 'LogisticIndepBeta' model with new data.
empty_data <- Data(doseGrid = seq(25, 300, 25))

my_model_lib <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)

# Then, we have some new observations data.
data <- Data(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  ID = 1:8,
  cohort = c(1L, 2L, 2L, 3L, 4L, 4L, 5L, 6L),
  doseGrid = empty_data@doseGrid
)

# Update the model to get new estimates.
new_model_lib <- update(object = my_model_lib, data = data)

# Update the 'Effloglog' model with new data.
empty_data_dual <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)

my_model_eff <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = empty_data_dual,
  const = 0
)

# Data with new observations data.
my_data_dual <- DataDual(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  ID = 1:8,
  cohort = c(1L, 2L, 2L, 3L, 4L, 4L, 5L, 6L),
  doseGrid = empty_data_dual@doseGrid
)

# Update the model to get new estimates.
new_model_eff <- update(object = my_model_eff, data = my_data_dual)
# Update the 'LogisticIndepBeta' model with new data.
empty_data <- Data(doseGrid = seq(25, 300, 25))

my_model_lib <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = empty_data
)

# Then, we have some new observations data.
data <- Data(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  ID = 1:8,
  cohort = c(1L, 2L, 2L, 3L, 4L, 4L, 5L, 6L),
  doseGrid = empty_data@doseGrid
)

# Update the model to get new estimates.
new_model_lib <- update(object = my_model_lib, data = data)

# Update the 'Effloglog' model with new data.
empty_data_dual <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)

my_model_eff <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = empty_data_dual,
  const = 0
)

# Data with new observations data.
my_data_dual <- DataDual(
  x = c(25, 50, 50, 75, 100, 100, 225, 300),
  y = c(0, 0, 0, 0, 1, 1, 1, 1),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  ID = 1:8,
  cohort = c(1L, 2L, 2L, 3L, 4L, 4L, 5L, 6L),
  doseGrid = empty_data_dual@doseGrid
)

# Update the model to get new estimates.
new_model_eff <- update(object = my_model_eff, data = my_data_dual)

Internal Helper Functions for Validation of `Backfill` Objects

Description

These functions are only used internally to validate the format of an input Backfill object and therefore not exported.

Usage

v_backfill(object)
v_backfill(object)

Arguments

object

(Backfill)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_backfill(): validates that the Backfill object contains valid slots.

Internal Helper Functions for Validation of `CohortSize` Objects

Description

These functions are only used internally to validate the format of an input CohortSize or inherited classes and therefore not exported.

Usage

v_cohort_size_range(object)

v_cohort_size_dlt(object)

v_cohort_size_const(object)

v_cohort_size_random(object)

v_cohort_size_parts(object)

v_cohort_size_max(object)
v_cohort_size_range(object)

v_cohort_size_dlt(object)

v_cohort_size_const(object)

v_cohort_size_random(object)

v_cohort_size_parts(object)

v_cohort_size_max(object)

Arguments

object

(CohortSize)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_cohort_size_range(): validates that the CohortSizeRange object contains valid intervals and cohort_size slots.
v_cohort_size_dlt(): validates that the CohortSizeDLT object contains valid intervals and cohort_size slots.
v_cohort_size_const(): validates that the CohortSizeConst object contains valid size slot.
v_cohort_size_random(): validates that the CohortSizeRandom object contains valid min_size and max_size slots.
v_cohort_size_parts(): validates that the CohortSizeParts object contains valid sizes slot.
v_cohort_size_max(): validates that the CohortSizeMax object contains valid cohort_sizes slot.

Internal Helper Functions for Validation of `GeneralData` Objects

Description

These functions are only used internally to validate the format of an input GeneralData or inherited classes and therefore not exported.

Usage

v_general_data(object)

h_doses_unique_per_cohort(dose, cohort)

v_data(object)

v_data_dual(object)

v_data_parts(object)

v_data_mixture(object)

v_data_da(object)

v_data_ordinal(object)

v_data_grouped(object)
v_general_data(object)

h_doses_unique_per_cohort(dose, cohort)

v_data(object)

v_data_dual(object)

v_data_parts(object)

v_data_mixture(object)

v_data_da(object)

v_data_ordinal(object)

v_data_grouped(object)

Arguments

object

(GeneralData)
object to validate.

dose

(numeric)
dose values.

cohort

(integer)
cohort indices parallel to doses.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

TRUE if dose is unique per cohort, otherwise FALSE.

Functions

v_general_data(): validates that the GeneralData object contains unique ID, non-negative cohort indices and ID and cohort vectors are of the same length nObs.
h_doses_unique_per_cohort(): helper function which verifies whether the dose values are unique in each and every different cohort.
v_data(): validates that the Data object contains valid elements with respect to their types, dependency and length.
v_data_dual(): validates that the DataDual object contains valid biomarker vector with respect to its type and the length.
v_data_parts(): validates that the DataParts object contains valid elements with respect to their types, dependency and length.
v_data_mixture(): validates that the DataMixture object contains valid elements with respect to their types, dependency and length.
v_data_da(): validates that the DataDA object contains valid elements with respect to their types, dependency and length.
v_data_ordinal(): validates that the DataOrdinal object contains valid elements with respect to their types, dependency and length.
v_data_grouped(): validates that the DataGrouped object contains valid group information.

Internal Helper Functions for Validation of `RuleDesign` Objects

Description

These functions are only used internally to validate the format of an input RuleDesign or inherited classes and therefore not exported.

These functions are only used internally to validate the format of an input RuleDesignOrdinal or inherited classes and therefore not exported.

Usage

v_rule_design(object)

v_rule_design_ordinal(object)

v_design_grouped(object)
v_rule_design(object)

v_rule_design_ordinal(object)

v_design_grouped(object)

Arguments

object

(RuleDesignOrdinal)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_rule_design(): validates that the RuleDesign object contains valid startingDose.
v_rule_design_ordinal(): validates that the RuleDesignOrdinal object contains valid starting_dose.
v_design_grouped(): validates that the DesignGrouped object contains valid flags.

Internal Helper Functions for Validation of `GeneralSimulations` Objects

Description

These functions are only used internally to validate the format of an input GeneralSimulations or inherited classes and therefore not exported.

Usage

v_general_simulations(object)

v_simulations(object)

v_dual_simulations(object)

v_da_simulations(object)
v_general_simulations(object)

v_simulations(object)

v_dual_simulations(object)

v_da_simulations(object)

Arguments

object

(GeneralSimulations)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_general_simulations(): validates that the GeneralSimulations object contains valid data object and valid dose simulations.
v_simulations(): validates that the Simulations object contains valid object fit, stop_reasons, stop_report, and additional_stats compared to the general class GeneralSimulations.
v_dual_simulations(): validates that the DualSimulations object and capture the dose-biomarker fits, and the sigma2W and rho estimates.
v_da_simulations(): validates that the DASimulations object contains valid trial_duration the vector of trial duration values for all simulations.

Internal Helper Functions for Validation of `Increments` Objects

Description

These functions are only used internally to validate the format of an input Increments or inherited classes and therefore not exported.

Usage

v_increments_relative(object)

v_increments_relative_parts(object)

v_increments_relative_dlt(object)

v_increments_dose_levels(object)

v_increments_hsr_beta(object)

v_increments_min(object)

v_increments_maxtoxprob(object)

v_increments_ordinal(object)

v_cohort_size_ordinal(object)
v_increments_relative(object)

v_increments_relative_parts(object)

v_increments_relative_dlt(object)

v_increments_dose_levels(object)

v_increments_hsr_beta(object)

v_increments_min(object)

v_increments_maxtoxprob(object)

v_increments_ordinal(object)

v_cohort_size_ordinal(object)

Arguments

object

(Increments)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_increments_relative(): validates that the IncrementsRelative object contains valid intervals and increments parameters.
v_increments_relative_parts(): validates that the IncrementsRelativeParts object contains valid dlt_start and clean_start parameters.
v_increments_relative_dlt(): validates that the IncrementsRelativeDLT object contains valid intervals and increments parameters.
v_increments_dose_levels(): validates that the IncrementsDoseLevels object contains valid levels and basis_level option.
v_increments_hsr_beta(): validates that the IncrementsHSRBeta object contains valid probability target, threshold and shape parameters.
v_increments_min(): validates that the IncrementsMin object contains a list with Increments objects.
v_increments_maxtoxprob(): validates the IncrementsMaxToxProb
v_increments_ordinal(): validates that the IncrementsOrdinal object contains valid grade and standard Increments rule.
v_cohort_size_ordinal(): validates that the CohortSizeOrdinal object contains valid grade and standard CohortSize rule.

Internal Helper Functions for Validation of `McmcOptions` Objects

Description

These functions are only used internally to validate the format of an input McmcOptions or inherited classes and therefore not exported.

Usage

v_mcmc_options(object)
v_mcmc_options(object)

Arguments

object

(McmcOptions)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_mcmc_options(): validates that the McmcOptions object contains valid integer scalars iterations, burnin and step as well as proper parameters for Random Number Generator.

Internal Helper Functions for Validation of `GeneralModel` and `ModelPseudo` Objects

Description

These functions are only used internally to validate the format of an input GeneralModel and ModelPseudo or inherited classes and therefore are not exported.

Usage

v_general_model(object)

v_model_logistic_kadane(object)

v_model_logistic_kadane_beta_gamma(object)

v_model_logistic_normal_mix(object)

v_model_logistic_normal_fixed_mix(object)

v_model_logistic_log_normal_mix(object)

v_model_dual_endpoint(object)

v_model_dual_endpoint_rw(object)

v_model_dual_endpoint_beta(object)

v_model_dual_endpoint_emax(object)

v_model_logistic_indep_beta(object)

v_model_eff_log_log(object)

v_model_eff_flexi(object)

v_model_da_logistic_log_normal(object)

v_model_tite_logistic_log_normal(object)

v_model_one_par_exp_normal_prior(object)

v_model_one_par_exp_prior(object)

v_logisticlognormalordinal(object)
v_general_model(object)

v_model_logistic_kadane(object)

v_model_logistic_kadane_beta_gamma(object)

v_model_logistic_normal_mix(object)

v_model_logistic_normal_fixed_mix(object)

v_model_logistic_log_normal_mix(object)

v_model_dual_endpoint(object)

v_model_dual_endpoint_rw(object)

v_model_dual_endpoint_beta(object)

v_model_dual_endpoint_emax(object)

v_model_logistic_indep_beta(object)

v_model_eff_log_log(object)

v_model_eff_flexi(object)

v_model_da_logistic_log_normal(object)

v_model_tite_logistic_log_normal(object)

v_model_one_par_exp_normal_prior(object)

v_model_one_par_exp_prior(object)

v_logisticlognormalordinal(object)

Arguments

object

(GeneralModel) or (ModelPseudo)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_general_model(): validates that the names of the arguments in init function are included in datanames or datanames_prior slots.
v_model_logistic_kadane(): validates that the logistic Kadane model parameters are valid.
v_model_logistic_kadane_beta_gamma(): validates that the logistic Kadane model parameters with a beta and gamma prior are valid.
v_model_logistic_normal_mix(): validates that weightpar is valid.
v_model_logistic_normal_fixed_mix(): validates that component is a list with valid ModelParamsNormal objects as well as weights are correct.
v_model_logistic_log_normal_mix(): validates that share_weight represents probability.
v_model_dual_endpoint(): validates that DualEndpoint class slots are valid.
v_model_dual_endpoint_rw(): validates that DualEndpointRW class slots are valid.
v_model_dual_endpoint_beta(): validates that DualEndpointBeta class slots are valid.
v_model_dual_endpoint_emax(): validates that DualEndpointEmax class slots are valid.
v_model_logistic_indep_beta(): validates that LogisticIndepBeta class slots are valid.
v_model_eff_log_log(): validates that Effloglog class slots are valid.
v_model_eff_flexi(): validates that EffFlexi class slots are valid.
v_model_da_logistic_log_normal(): validates that DALogisticLogNormal class slots are valid.
v_model_tite_logistic_log_normal(): validates that TITELogisticLogNormal class slots are valid.
v_model_one_par_exp_normal_prior(): validates that OneParLogNormalPrior class slots are valid.
v_model_one_par_exp_prior(): validates that OneParExpPrior class slots are valid.
v_logisticlognormalordinal(): confirms that cov is diagonal

Internal Helper Functions for Validation of Model Parameters Objects

Description

These functions are only used internally to validate the format of an object with model parameters or inherited classes and therefore not exported.

Usage

v_model_params_normal(object)
v_model_params_normal(object)

Arguments

object

(ModelParamsNormal)
multivariate normal parameters object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_model_params_normal(): a helper function that validates multivariate normal parameters.

Internal Helper Functions for Validation of `NextBest` Objects

Description

These functions are only used internally to validate the format of an input NextBest or inherited classes and therefore not exported.

Usage

v_next_best_mtd(object)

v_next_best_ncrm(object)

v_next_best_ncrm_loss(object)

v_next_best_dual_endpoint(object)

v_next_best_min_dist(object)

v_next_best_ewoc(object)

v_next_best_inf_theory(object)

v_next_best_td(object)

v_next_best_td_samples(object)

v_next_best_max_gain_samples(object)

v_next_best_prob_mtd_lte(object)

v_next_best_prob_mtd_min_dist(object)

v_next_best_ordinal(object)
v_next_best_mtd(object)

v_next_best_ncrm(object)

v_next_best_ncrm_loss(object)

v_next_best_dual_endpoint(object)

v_next_best_min_dist(object)

v_next_best_ewoc(object)

v_next_best_inf_theory(object)

v_next_best_td(object)

v_next_best_td_samples(object)

v_next_best_max_gain_samples(object)

v_next_best_prob_mtd_lte(object)

v_next_best_prob_mtd_min_dist(object)

v_next_best_ordinal(object)

Arguments

object

(NextBest)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_next_best_mtd(): validates that the NextBestMTD object contains valid target probability and derive function.
v_next_best_ncrm(): validates that the NextBestNCRM object contains valid target probability, overdose and max_overdose_prob probability ranges.
v_next_best_ncrm_loss(): validates that the NextBestNCRMLoss object contains valid objects.
v_next_best_dual_endpoint(): validates that the NextBestDualEndpoint object contains valid probability objects.
v_next_best_min_dist(): validates that the NextBestMinDist object contains valid target object.
v_next_best_ewoc(): validates that the NextBestEWOC object contains valid target, overdose and max_overdose_prob parameters.
v_next_best_inf_theory(): validates that the NextBestInfTheory object contains valid target and asymmetry objects.
v_next_best_td(): validates that the NextBestTD object contains valid prob_target_drt and prob_target_eot probabilities.
v_next_best_td_samples(): validates that the NextBestTDsamples object contains valid derive function.
v_next_best_max_gain_samples(): validates that the NextBestMaxGainSamples object contains valid derive and mg_derive functions.
v_next_best_prob_mtd_lte(): validates that the NextBestProbMTDLTE object contains valid target probability and method string value.
v_next_best_prob_mtd_min_dist(): validates that the NextBestProbMTDMinDist object contains valid target probability and method string value.
v_next_best_ordinal(): validates that the NextBestOrdinal object contains valid grade and standard NextBest rule.

Internal Helper Functions for Validation of `Opening` Objects

Description

These functions are only used internally to validate the format of an input Opening or inherited classes and therefore not exported.

Usage

v_opening_min_dose(object)

v_opening_min_cohorts(object)

v_opening_min_responses(object)
v_opening_min_dose(object)

v_opening_min_cohorts(object)

v_opening_min_responses(object)

Arguments

object

(Opening)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_opening_min_dose(): validates that the OpeningMinDose object contains valid min_dose slot.
v_opening_min_cohorts(): validates that the OpeningMinCohorts object contains valid min_cohorts slot.
v_opening_min_responses(): validates that the OpeningMinResponses object contains valid min_responses and include_lower_doses slots.

Internal Helper Functions for Validation of `PseudoSimulations` Objects

Description

These functions are only used internally to validate the format of an input PseudoSimulations or inherited classes and therefore not exported.

Usage

v_pseudo_simulations(object)

v_pseudo_dual_simulations(object)

v_pseudo_dual_flex_simulations(object)
v_pseudo_simulations(object)

v_pseudo_dual_simulations(object)

v_pseudo_dual_flex_simulations(object)

Arguments

object

(PseudoSimulations)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_pseudo_simulations(): validates that the PseudoSimulations object contains valid fit, FinalTDtargetEndOfTrialEstimates , FinalTDtargetDuringTrialAtDoseGrid,FinalTDtargetEndOfTrialAtDoseGrid , FinalTDEOTCIs, FinalTDEOTRatios, FinalCIs, FinalRatios, object and valid stopReasons simulations.
v_pseudo_dual_simulations(): validates that the PseudoDualSimulations object contains valid fit_eff, final_gstar_estimates , final_gstar_at_dose_grid, final_gstar_cis , final_gstar_ratios, final_optimal_dose, final_optimal_dose_at_dose_grid object and valid sigma2_est simulations.
v_pseudo_dual_flex_simulations(): validates that the PseudoDualFlexiSimulations object contains valid sigma2_beta_w_est vector of the final posterior mean sigma2betaW estimates.FinalGstarEstimates , FinalGstarAtDoseGrid,

Internal Helper Functions for Validation of `Recruitment` Objects

Description

These functions are only used internally to validate the format of an input Recruitment or inherited classes and therefore not exported.

Usage

v_recruitment_ratio(object)
v_recruitment_ratio(object)

Arguments

object

(Recruitment) object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_recruitment_ratio(): validates that the RecruitmentRatio object contains valid ratio slot.

Internal Helper Functions for Validation of `SafetyWindow` Objects

Description

These functions are only used internally to validate the format of an input SafetyWindow or inherited classes and therefore not exported.

Usage

v_safety_window_size(object)

v_safety_window_const(object)
v_safety_window_size(object)

v_safety_window_const(object)

Arguments

object

(SafetyWindow)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_safety_window_size(): validates that the SafetyWindowSize object contains valid slots.
v_safety_window_const(): validates that the SafetyWindowConst object contains valid slots.

Internal Helper Functions for Validation of `Samples` Objects

Description

These functions are only used internally to validate the format of an input Samples or inherited classes and therefore not exported.

Usage

v_samples(object)
v_samples(object)

Arguments

object

(Samples)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_samples(): validates that the Samples object contains valid data slot.

Internal Helper Functions for Validation of `Stopping` Objects

Description

These functions are only used internally to validate the format of an input Stopping or inherited classes and therefore not exported.

Usage

v_stopping_cohorts_near_dose(object)

v_stopping_patients_near_dose(object)

v_stopping_min_cohorts(object)

v_stopping_min_patients(object)

v_stopping_target_prob(object)

v_stopping_mtd_distribution(object)

v_stopping_mtd_cv(object)

v_stopping_target_biomarker(object)

v_stopping_list(object)

v_stopping_all(object)

v_stopping_tdci_ratio(object)
v_stopping_cohorts_near_dose(object)

v_stopping_patients_near_dose(object)

v_stopping_min_cohorts(object)

v_stopping_min_patients(object)

v_stopping_target_prob(object)

v_stopping_mtd_distribution(object)

v_stopping_mtd_cv(object)

v_stopping_target_biomarker(object)

v_stopping_list(object)

v_stopping_all(object)

v_stopping_tdci_ratio(object)

Arguments

object

(Stopping)
object to validate.

Value

A character vector with the validation failure messages, or TRUE in case validation passes.

Functions

v_stopping_cohorts_near_dose(): validates that the StoppingCohortsNearDose object contains valid nCohorts and percentage parameters.
v_stopping_patients_near_dose(): validates that the StoppingPatientsNearDose object contains valid nPatients and percentage parameters.
v_stopping_min_cohorts(): validates that the StoppingMinCohorts object contains valid nCohorts parameter.
v_stopping_min_patients(): validates that the StoppingMinPatients object contains valid nPatients parameter.
v_stopping_target_prob(): validates that the StoppingTargetProb object contains valid target and prob parameters.
v_stopping_mtd_distribution(): validates that the StoppingMTDdistribution object contains valid target, thresh and prob parameters.
v_stopping_mtd_cv(): validates that the StoppingMTDCV object contains valid probability target and percentage threshold.
v_stopping_target_biomarker(): validates that the StoppingTargetBiomarker object contains valid target, is_relative and probslots.
v_stopping_list(): validates that the StoppingList object contains valid stop_list, summary slots.
v_stopping_all(): validates that the StoppingAll object contains valid stop_list slot.
v_stopping_tdci_ratio(): validates that the StoppingTDCIRatio object contains valid target_ratio and prob_target slots.

`Validate`

Description

The Validate class is a Reference Class to help programming validation for new S4 classes.

Details

Starting from an empty msg vector, with each check that is returning FALSE the vector gets a new element - the string explaining the failure of the validation.

Fields

msg: (character)
the cumulative messages.

Methods

check(test, string = ""): Check whether the test is TRUE; if so, return NULL. Otherwise, add the string message into the cumulative messages vector msg.
result(): Return either cumulative messages vector msg (which contains the error messages from all the checks), or NULL, if msg is empty (i.e. all the checks were successful).

Determine the Safety Window Length of the Next Cohort

Description

This function determines the safety window length of the next cohort.

Usage

windowLength(safetyWindow, size, ...)

## S4 method for signature 'SafetyWindowSize'
windowLength(safetyWindow, size, data, ...)

## S4 method for signature 'SafetyWindowConst'
windowLength(safetyWindow, size, ...)
windowLength(safetyWindow, size, ...)

## S4 method for signature 'SafetyWindowSize'
windowLength(safetyWindow, size, data, ...)

## S4 method for signature 'SafetyWindowConst'
windowLength(safetyWindow, size, ...)

Arguments

safetyWindow

(SafetyWindow)
the rule, an object of class SafetyWindow.

size

(integer)
the next cohort size.

...

additional arguments without method dispatch.

data

(DataDA)
the data input, an object of class DataDA.

Value

The windowLength as a list of safety window parameters (gap, follow, follow_min).

Functions

windowLength(SafetyWindowSize): Determine safety window length based on the cohort size.
windowLength(SafetyWindowConst): Constant safety window length.

Examples

# nolint start

# Create the data
data <- DataDA(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60
)

# Initialize the CRM model used to model the data
npiece_ <- 10
lambda_prior <- function(k) {
  npiece_ / (data@Tmax * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(
    as.matrix(c(1:npiece_), 1, npiece_),
    2,
    lambda_prior
  ))),
  c_par = 2
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the rule which will be used to select the next cohort size
# based on the class 'CohortSizeConst'
mySize <- CohortSizeConst(size = 3)

# Determine the cohort size for the next cohort
sizeRecommendation <- size(mySize, dose = doseRecommendation$value, data = data)

# Rule for the safety window length:
#   -having patientGap as (0,7,3,3,...) for cohort size <4
#   -and having patientGap as (0,9,5,5,...) for cohort size >=4
myWindowLength <- SafetyWindowSize(
  gap = list(c(7, 3), c(9, 5)),
  size = c(1, 4),
  follow = 7,
  follow_min = 14
)

# Determine the safety window parameters for the next cohort
windowLength(myWindowLength, size = sizeRecommendation)

# nolint end
# nolint start

# Create the data
data <- DataDA(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60
)

# Initialize the CRM model used to model the data
npiece_ <- 10
lambda_prior <- function(k) {
  npiece_ / (data@Tmax * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(
    as.matrix(c(1:npiece_), 1, npiece_),
    2,
    lambda_prior
  ))),
  c_par = 2
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the rule which will be used to select the next cohort size
# based on the class 'CohortSizeConst'
mySize <- CohortSizeConst(size = 3)

# Determine the cohort size for the next cohort
sizeRecommendation <- size(mySize, dose = doseRecommendation$value, data = data)

# Rule for having safety window length with constant safety window parameters
myWindowLength <- SafetyWindowConst(gap = c(7, 3), follow = 7, follow_min = 14)

# Determine the safety window parameters for the next cohort
windowLength(myWindowLength, size = sizeRecommendation)

# nolint end
# nolint start

# Create the data
data <- DataDA(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60
)

# Initialize the CRM model used to model the data
npiece_ <- 10
lambda_prior <- function(k) {
  npiece_ / (data@Tmax * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(
    as.matrix(c(1:npiece_), 1, npiece_),
    2,
    lambda_prior
  ))),
  c_par = 2
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the rule which will be used to select the next cohort size
# based on the class 'CohortSizeConst'
mySize <- CohortSizeConst(size = 3)

# Determine the cohort size for the next cohort
sizeRecommendation <- size(mySize, dose = doseRecommendation$value, data = data)

# Rule for the safety window length:
#   -having patientGap as (0,7,3,3,...) for cohort size <4
#   -and having patientGap as (0,9,5,5,...) for cohort size >=4
myWindowLength <- SafetyWindowSize(
  gap = list(c(7, 3), c(9, 5)),
  size = c(1, 4),
  follow = 7,
  follow_min = 14
)

# Determine the safety window parameters for the next cohort
windowLength(myWindowLength, size = sizeRecommendation)

# nolint end
# nolint start

# Create the data
data <- DataDA(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  doseGrid = c(0.1, 0.5, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60
)

# Initialize the CRM model used to model the data
npiece_ <- 10
lambda_prior <- function(k) {
  npiece_ / (data@Tmax * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(
    as.matrix(c(1:npiece_), 1, npiece_),
    2,
    lambda_prior
  ))),
  c_par = 2
)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)
nextMaxDose <- maxDose(myIncrements, data = data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

# Calculate the next best dose
doseRecommendation <- nextBest(
  myNextBest,
  doselimit = nextMaxDose,
  samples = samples,
  model = model,
  data = data
)

# Define the rule which will be used to select the next cohort size
# based on the class 'CohortSizeConst'
mySize <- CohortSizeConst(size = 3)

# Determine the cohort size for the next cohort
sizeRecommendation <- size(mySize, dose = doseRecommendation$value, data = data)

# Rule for having safety window length with constant safety window parameters
myWindowLength <- SafetyWindowConst(gap = c(7, 3), follow = 7, follow_min = 14)

# Determine the safety window parameters for the next cohort
windowLength(myWindowLength, size = sizeRecommendation)

# nolint end

Package 'crmPack'

Help Index

CohortSize

Description

Usage

Note

See Also

Combine Two Stopping Rules with AND

Description

Usage

Arguments

Value

Examples

Combine an Atomic Stopping Rule and a Stopping List with AND

Description

Usage

Arguments

Value

Examples

Combine a Stopping List and an Atomic Stopping Rule with AND

Description

Usage

Arguments

Value

Examples

Logical AND Operator for Opening Objects

Description

Usage

Arguments

Value

See Also

Approximate posterior with (log) normal distribution

Description

Usage

Arguments

Value

Functions

Examples

Additional Assertions for checkmate

Description

Value

See Also

Backfill class

Description

Usage

Arguments

Value

Slots

Note

Get the Biomarker Levels for a Given Dual-Endpoint Model, Given Dose Levels and Samples

Description

Usage

Arguments

Details

Value

Functions

Examples

Check if All Arguments Are Equal

Description

Usage

Arguments

Value

Note

See Also

Examples

Check that an argument is a valid format specification

Description

Usage

Arguments

Value

See Also

Examples

Check if vectors are of compatible lengths

Description

Usage

Arguments

Value

See Also

Examples

Check if an argument is a probability vector

`CohortSize`

Additional Assertions for `checkmate`

`Backfill` class

`CohortSizeConst`

`CohortSizeDLT`

`CohortSizeMax`

`CohortSizeMin`

`CohortSizeOrdinal`

`CohortSizeParts`