Package 'teal.modules.clinical'

Description

Provides teal modules for the standard clinical trials outputs. The teal modules add an encoding panel to interactively change the encodings within teal.

Author(s)

Maintainer: Joe Zhu [email protected]

Authors:

• Jana Stoilova [email protected]

• Davide Garolini [email protected]

• Emily de la Rua [email protected]

• Abinaya Yogasekaram [email protected]

• Mahmoud Hallal [email protected]

• Dawid Kaledkowski [email protected]

• Rosemary Li [email protected]

• Heng Wang [email protected]

• Pawel Rucki [email protected]

• Nikolas Burkoff

• Konrad Pagacz

Other contributors:

• Vaakesan Sundrelingam [contributor]

• Francois Collin [contributor]

• Imanol Zubizarreta [contributor]

• F. Hoffmann-La Roche AG [copyright holder, funder]

See Also

Useful links:

• https://insightsengineering.github.io/teal.modules.clinical/

• https://github.com/insightsengineering/teal.modules.clinical/

• Report bugs at https://github.com/insightsengineering/teal.modules.clinical/issues

Description

Add a new expression to a list (of expressions).

Usage

add_expr(expr_ls, new_expr)

Arguments

Details

Offers a stricter control to add new expressions to an existing list. The list of expressions can be later used to generate a pipeline, for instance with pipe_expr.

Value

a list of call.

Examples

library(rtables)

lyt <- list()
lyt <- add_expr(lyt, substitute(basic_table()))
lyt <- add_expr(
  lyt, substitute(split_cols_by(var = arm), env = list(armcd = "ARMCD"))
)
lyt <- add_expr(
  lyt,
  substitute(
    test_proportion_diff(
      vars = "rsp", method = "cmh", variables = list(strata = "strata")
    )
  )
)
lyt <- add_expr(lyt, quote(build_table(df = dta)))
pipe_expr(lyt)

Description

Generic to parse text into numeric vectors. This was initially designed for a robust interpretation of text input in teal modules.

Usage

as_num(str)

## Default S3 method:
as_num(str)

## S3 method for class 'character'
as_num(str)

## S3 method for class 'numeric'
as_num(str)

## S3 method for class 'factor'
as_num(str)

## S3 method for class 'logical'
as_num(str)

Arguments

Details

The function is intended to extract any numeric from a character string, factor levels, boolean and return a vector of numeric.

Value

As vector of numeric if directly parsed from numeric or boolean. A list of numeric if parsed from a character string, each character string associated with an list item.

Examples

dta <- list(
  character = c("text10,20.5letter30.!", "!-.40$$-50e5[", NA),
  factor    = factor(c("]+60e-6, 7.7%%8L", "%90sep.100\"1L", NA_character_)),
  numeric   = c(1, -5e+2, NA),
  logical   = c(TRUE, FALSE, NA)
)
lapply(dta, as_num)

Description

Groups several expressions in a single bracketed expression.

Usage

bracket_expr(exprs)

Arguments

Value

a ⁠{⁠ object. See base::Paren() for details.

Examples

adsl <- tmc_ex_adsl
adrs <- tmc_ex_adrs

expr1 <- substitute(
  expr = anl <- subset(df, PARAMCD == param),
  env = list(df = as.name("adrs"), param = "INVET")
)
expr2 <- substitute(expr = anl$rsp_lab <- d_onco_rsp_label(anl$AVALC))
expr3 <- substitute(
  expr = {
    anl$is_rsp <- anl$rsp_lab %in%
      c("Complete Response (CR)", "Partial Response (PR)")
  }
)

res <- bracket_expr(list(expr1, expr2, expr3))
eval(res)
table(anl$rsp_lab, anl$is_rsp)

Description

e.g. combine with + for ggplot without introducing parentheses due to associativity

Usage

call_concatenate(args, bin_op = "+")

Arguments

Value

a call

Examples

library(ggplot2)

# What we want to achieve
call("+", quote(f), quote(g))
call("+", quote(f), call("+", quote(g), quote(h))) # parentheses not wanted
call("+", call("+", quote(f), quote(g)), quote(h)) # as expected without unnecessary parentheses
Reduce(function(existing, new) call("+", existing, new), list(quote(f), quote(g), quote(h)))

# how we do it
call_concatenate(list(quote(f), quote(g), quote(h)))
call_concatenate(list(quote(f)))
call_concatenate(list())
call_concatenate(
  list(quote(ggplot(mtcars)), quote(geom_point(aes(wt, mpg))))
)

eval(
  call_concatenate(
    list(quote(ggplot(mtcars)), quote(geom_point(aes(wt, mpg))))
  )
)

Description

Cleaning categorical variable descriptions before presenting.

Usage

clean_description(x)

Arguments

Value

a string

Examples

clean_description("Level A (other text)")
clean_description("A long string that should be shortened")

Description

Map value and level characters to values with with proper html tags, colors and icons.

Usage

color_lab_values(
  x,
  classes = c("HIGH", "NORMAL", "LOW"),
  colors = list(HIGH = "red", NORMAL = "grey", LOW = "blue"),
  default_color = "black",
  icons = list(HIGH = "glyphicon glyphicon-arrow-up", LOW =
    "glyphicon glyphicon-arrow-down")
)

Arguments

Value

a character vector where each element is a formatted HTML tag corresponding to a value in x.

Examples

color_lab_values(c("LOW", "LOW", "HIGH", "NORMAL", "HIGH"))

Description

Get full label, useful for annotating plots

Usage

column_annotation_label(dataset, column, omit_raw_name = FALSE)

Arguments

Value

"Label ⁠[Column name]⁠" if label exists, otherwise "Column name".

Examples

data <- mtcars
column_annotation_label(data, "cyl")
attr(data[["cyl"]], "label") <- "Cylinder"
column_annotation_label(data, "cyl")
column_annotation_label(data, "cyl", omit_raw_name = TRUE)
column_annotation_label(tmc_ex_adsl, "ACTARM")

Description

Convert choices_selected to data_extract_spec with only filter_spec

Usage

cs_to_des_filter(
  cs,
  dataname,
  multiple = FALSE,
  include_vars = FALSE,
  label = "Filter by"
)

Arguments

Value

(teal.transform::data_extract_spec())

Description

Convert choices_selected to data_extract_spec with only select_spec

Usage

cs_to_des_select(
  cs,
  dataname,
  multiple = FALSE,
  ordered = FALSE,
  label = "Select"
)

Arguments

Value

(teal.transform::data_extract_spec())

Description

Convert choices_selected to filter_spec

Usage

cs_to_filter_spec(cs, multiple = FALSE, label = "Filter by")

Arguments

Value

(teal.transform::filter_spec())

Description

Convert choices_selected to select_spec

Usage

cs_to_select_spec(cs, multiple = FALSE, ordered = FALSE, label = "Select")

Arguments

Value

(select_spec)

Description

The default string used as a label for the "total" column. This value is used as the default value for the total_label argument throughout the teal.modules.clinical package. If not specified for each module by the user via the total_label argument, or in the R environment options via set_default_total_label(), then "All Patients" is used.

Usage

default_total_label()

set_default_total_label(total_label)

Arguments

Value

• default_total_label returns the current value if an R environment option has been set for "tmc_default_total_label", or "All Patients" otherwise.

• set_default_total_label has no return value.

Functions

• default_total_label(): Getter for default total column label.

• set_default_total_label(): Setter for default total column label. Sets the option "tmc_default_total_label" within the R environment.

Examples

# Default settings
default_total_label()
getOption("tmc_default_total_label")

# Set custom value
set_default_total_label("All Patients")

# Settings after value has been set
default_total_label()
getOption("tmc_default_total_label")

Description

Simulated CDISC Data for Examples

Usage

tmc_ex_adsl

tmc_ex_adae

tmc_ex_adaette

tmc_ex_adcm

tmc_ex_adeg

tmc_ex_adex

tmc_ex_adlb

tmc_ex_admh

tmc_ex_adqs

tmc_ex_adrs

tmc_ex_adtte

tmc_ex_advs

Format

rds (data.frame)

An object of class tbl_df (inherits from tbl, data.frame) with 200 rows and 26 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 541 rows and 51 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1800 rows and 35 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 512 rows and 45 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 5200 rows and 48 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 200 rows and 37 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 3000 rows and 58 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1077 rows and 33 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 7000 rows and 36 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1600 rows and 34 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1000 rows and 34 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 8400 rows and 34 columns.

Functions

• tmc_ex_adsl: ADSL data

• tmc_ex_adae: ADAE data

• tmc_ex_adaette: ADAETTE data

• tmc_ex_adcm: ADCM data

• tmc_ex_adeg: ADEG data

• tmc_ex_adex: ADEX data

• tmc_ex_adlb: ADLB data

• tmc_ex_admh: ADMH data

• tmc_ex_adqs: ADQS data

• tmc_ex_adrs: ADRS data

• tmc_ex_adtte: ADTTE data

• tmc_ex_advs: ADVS data

Description

The data_extract_ui is located under extended html id. We could not use ns("original id") for reference, as it is extended with specific suffixes.

Usage

extract_input(varname, dataname, filter = FALSE)

Arguments

Value

a string

Examples

extract_input("ARM", "ADSL")

Description

Usage

get_var_labels(datasets, dataname, vars)

Arguments

Value

character variable labels.

Description

Deparse an expression into a string.

Usage

h_concat_expr(expr)

Arguments

Value

a string.

Examples

expr <- quote({
  library(rtables)
  basic_table() %>%
    split_cols_by(var = "ARMCD") %>%
    test_proportion_diff(
      vars = "rsp", method = "cmh", variables = list(strata = "strata")
    ) %>%
    build_table(df = dta)
})

h_concat_expr(expr)

Whether object is of class teal.transform::choices_selected()

Description

Whether object is of class teal.transform::choices_selected()

Usage

is.cs_or_des(x)

Arguments

Value

(logical)

Description

Concatenate expressions in a single pipeline-flavor expression.

Usage

pipe_expr(exprs, pipe_str = "%>%")

Arguments

Value

a call

Examples

pipe_expr(
  list(
    expr1 = substitute(df),
    expr2 = substitute(head)
  )
)

Description

The function generate the standard expression for pre-processing of dataset in teal module applications. This is especially of interest when the same preprocessing steps needs to be applied similarly to several datasets (e.g. ADSL and ADRS).

Usage

prepare_arm(
  dataname,
  arm_var,
  ref_arm,
  comp_arm,
  compare_arm = !is.null(ref_arm),
  ref_arm_val = paste(ref_arm, collapse = "/"),
  drop = TRUE
)

Arguments

Details

In teal.modules.clinical, the user interface includes manipulation of the study arms. Classically: the arm variable itself (e.g. ARM, ACTARM), the reference arm (0 or more), the comparison arm (1 or more) and the possibility to combine comparison arms.

Note that when no arms should be compared with each other, then the produced expression is reduced to optionally dropping non-represented levels of the arm.

When comparing arms, the pre-processing includes three steps:

1. Filtering of the dataset to retain only the arms of interest (reference and comparison).

2. Optional, if more than one arm is designated as reference they are combined into a single level.

3. The reference is explicitly reassigned and the non-represented levels of arm are dropped.

Value

a call

Examples

prepare_arm(
  dataname = "adrs",
  arm_var = "ARMCD",
  ref_arm = "ARM A",
  comp_arm = c("ARM B", "ARM C")
)

prepare_arm(
  dataname = "adsl",
  arm_var = "ARMCD",
  ref_arm = c("ARM B", "ARM C"),
  comp_arm = "ARM A"
)

Description

This function generates the standard expression for pre-processing of dataset arm levels in and is used to apply the same steps in safety teal modules.

Usage

prepare_arm_levels(dataname, parentname, arm_var, drop_arm_levels = TRUE)

Arguments

Value

a ⁠{⁠ object. See base::Paren() for details.

Examples

prepare_arm_levels(
  dataname = "adae",
  parentname = "adsl",
  arm_var = "ARMCD",
  drop_arm_levels = TRUE
)

prepare_arm_levels(
  dataname = "adae",
  parentname = "adsl",
  arm_var = "ARMCD",
  drop_arm_levels = FALSE
)

Description

Split choices_selected objects with interactions into their component variables

Usage

split_choices(x)

Arguments

Value

a teal.transform::choices_selected() object.

Note

uses the regex ⁠\\*|:⁠ to perform the split.

Examples

split_choices(choices_selected(choices = c("x:y", "a*b"), selected = all_choices()))

Description

Renders the expression for column split in rtables depending on:

• the expected or not arm comparison

• the expected or not arm combination

Usage

split_col_expr(compare, combine, ref, arm_var)

Arguments

Value

a call

Examples

split_col_expr(
  compare = TRUE,
  combine = FALSE,
  ref = "ARM A",
  arm_var = "ARMCD"
)

Description

Split interaction terms into their component variables

Usage

split_interactions(x, by = "\\*|:")

Arguments

Value

a vector of strings where each element is a component variable extracted from interaction term x.

Examples

split_interactions("x:y")
split_interactions("x*y")

Description

This module produces an analysis table using Generalized Estimating Equations (GEE).

Usage

tm_a_gee(
  label,
  dataname,
  parentname = ifelse(inherits(arm_var, "data_extract_spec"),
    teal.transform::datanames_input(arm_var), "ADSL"),
  aval_var,
  id_var,
  arm_var,
  visit_var,
  cov_var,
  arm_ref_comp = NULL,
  paramcd,
  conf_level = teal.transform::choices_selected(c(0.95, 0.9, 0.8), 0.95, keep_order =
    TRUE),
  pre_output = NULL,
  post_output = NULL,
  basic_table_args = teal.widgets::basic_table_args(),
  decorators = NULL
)

Arguments

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• table (ElementaryTable - output of rtables::build_table)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

See Also

The TLG Catalog where additional example apps implementing this module can be found.

Examples

library(dplyr)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADQS <- tmc_ex_adqs %>%
    filter(ABLFL != "Y" & ABLFL2 != "Y") %>%
    mutate(
      AVISIT = as.factor(AVISIT),
      AVISITN = rank(AVISITN) %>%
        as.factor() %>%
        as.numeric() %>%
        as.factor(),
      AVALBIN = AVAL < 50 # Just as an example to get a binary endpoint.
    ) %>%
    droplevels()
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

app <- init(
  data = data,
  modules = modules(
    tm_a_gee(
      label = "GEE",
      dataname = "ADQS",
      aval_var = choices_selected("AVALBIN", fixed = TRUE),
      id_var = choices_selected(c("USUBJID", "SUBJID"), "USUBJID"),
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      visit_var = choices_selected(c("AVISIT", "AVISITN"), "AVISIT"),
      paramcd = choices_selected(
        choices = value_choices(data[["ADQS"]], "PARAMCD", "PARAM"),
        selected = "FKSI-FWB"
      ),
      cov_var = choices_selected(c("BASE", "AGE", "SEX", "BASE:AVISIT"), NULL)
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces analysis tables and plots for Mixed Model Repeated Measurements.

Usage

tm_a_mmrm(
  label,
  dataname,
  parentname = ifelse(inherits(arm_var, "data_extract_spec"),
    teal.transform::datanames_input(arm_var), "ADSL"),
  aval_var,
  id_var,
  arm_var,
  visit_var,
  cov_var,
  arm_ref_comp = NULL,
  paramcd,
  method = teal.transform::choices_selected(c("Satterthwaite", "Kenward-Roger",
    "Kenward-Roger-Linear"), "Satterthwaite", keep_order = TRUE),
  conf_level = teal.transform::choices_selected(c(0.95, 0.9, 0.8), 0.95, keep_order =
    TRUE),
  plot_height = c(700L, 200L, 2000L),
  plot_width = NULL,
  total_label = default_total_label(),
  pre_output = NULL,
  post_output = NULL,
  basic_table_args = teal.widgets::basic_table_args(),
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

Arguments

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• lsmeans_plot (ggplot2)

• diagnostic_plot (TableTree- output from rtables::build_table)

• lsmeans_table (TableTree- output from rtables::build_table)

• covariance_table (TableTree- output from rtables::build_table)

• fixed_effects_table (TableTree- output from rtables::build_table)

• diagnostic_table (TableTree- output from rtables::build_table)

Decorators can be applied to all outputs or only to specific objects using a named list of teal_transform_module objects. The "default" name is reserved for decorators that are applied to all outputs. See code snippet below:

tm_a_mrmm(
   ..., # arguments for module
   decorators = list(
     default = list(teal_transform_module(...)), # applied to all outputs
     lsmeans_plot = list(teal_transform_module(...)) # applied only to `lsmeans_plot` output
     diagnostic_plot = list(teal_transform_module(...)) # applied only to `diagnostic_plot` output
     lsmeans_table = list(teal_transform_module(...)) # applied only to `lsmeans_table` output
     covariance_table = list(teal_transform_module(...)) # applied only to `covariance_table` output
     fixed_effects_table = list(teal_transform_module(...)) # applied only to `fixed_effects_table` output
     diagnostic_table = list(teal_transform_module(...)) # applied only to `diagnostic_table` output
   )
)

Examples in Shinylive

example-1

Open in Shinylive

Note

The ordering of the input data sets can lead to slightly different numerical results or different convergence behavior. This is a known observation with the used package lme4. However, once convergence is achieved, the results are reliable up to numerical precision.

See Also

The TLG Catalog where additional example apps implementing this module can be found.

Examples

library(dplyr)

arm_ref_comp <- list(
  ARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  )
)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADQS <- tmc_ex_adqs %>%
    filter(ABLFL != "Y" & ABLFL2 != "Y") %>%
    filter(AVISIT %in% c("WEEK 1 DAY 8", "WEEK 2 DAY 15", "WEEK 3 DAY 22")) %>%
    mutate(
      AVISIT = as.factor(AVISIT),
      AVISITN = rank(AVISITN) %>%
        as.factor() %>%
        as.numeric() %>%
        as.factor() #' making consecutive numeric factor
    )
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

app <- init(
  data = data,
  modules = modules(
    tm_a_mmrm(
      label = "MMRM",
      dataname = "ADQS",
      aval_var = choices_selected(c("AVAL", "CHG"), "AVAL"),
      id_var = choices_selected(c("USUBJID", "SUBJID"), "USUBJID"),
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      visit_var = choices_selected(c("AVISIT", "AVISITN"), "AVISIT"),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        choices = value_choices(data[["ADQS"]], "PARAMCD", "PARAM"),
        selected = "FKSI-FWB"
      ),
      cov_var = choices_selected(c("BASE", "AGE", "SEX", "BASE:AVISIT"), NULL)
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces a ggplot2::ggplot() type bar chart and summary table of counts per category.

Usage

tm_g_barchart_simple(
  x = NULL,
  fill = NULL,
  x_facet = NULL,
  y_facet = NULL,
  label = "Count Barchart",
  plot_options = NULL,
  plot_height = c(600L, 200L, 2000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

Arguments

Details

Categories can be defined up to four levels deep and are defined through the x, fill, x_facet, and y_facet parameters. Any parameters set to NULL (default) are ignored.

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• plot (ggplot2)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

See Also

The TLG Catalog where additional example apps implementing this module can be found.

Examples

library(nestcolor)
library(dplyr)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl %>%
    mutate(ITTFL = factor("Y") %>%
      with_label("Intent-To-Treat Population Flag"))
  ADAE <- tmc_ex_adae %>%
    filter(!((AETOXGR == 1) & (AESEV == "MILD") & (ARM == "A: Drug X")))
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADAE <- data[["ADAE"]]

app <- init(
  data = data,
  modules = modules(
    tm_g_barchart_simple(
      label = "ADAE Analysis",
      x = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          choices = variable_choices(
            ADSL,
            c(
              "ARM", "ACTARM", "SEX",
              "RACE", "ITTFL", "SAFFL", "STRATA2"
            )
          ),
          selected = "ACTARM",
          multiple = FALSE
        )
      ),
      fill = list(
        data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            choices = variable_choices(
              ADSL,
              c(
                "ARM", "ACTARM", "SEX",
                "RACE", "ITTFL", "SAFFL", "STRATA2"
              )
            ),
            selected = "SEX",
            multiple = FALSE
          )
        ),
        data_extract_spec(
          dataname = "ADAE",
          select = select_spec(
            choices = variable_choices(ADAE, c("AETOXGR", "AESEV", "AESER")),
            selected = NULL,
            multiple = FALSE
          )
        )
      ),
      x_facet = list(
        data_extract_spec(
          dataname = "ADAE",
          select = select_spec(
            choices = variable_choices(ADAE, c("AETOXGR", "AESEV", "AESER")),
            selected = "AETOXGR",
            multiple = FALSE
          )
        ),
        data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            choices = variable_choices(
              ADSL,
              c(
                "ARM", "ACTARM", "SEX",
                "RACE", "ITTFL", "SAFFL", "STRATA2"
              )
            ),
            selected = NULL,
            multiple = FALSE
          )
        )
      ),
      y_facet = list(
        data_extract_spec(
          dataname = "ADAE",
          select = select_spec(
            choices = variable_choices(ADAE, c("AETOXGR", "AESEV", "AESER")),
            selected = "AESEV",
            multiple = FALSE
          )
        ),
        data_extract_spec(
          dataname = "ADSL",
          select = select_spec(
            choices = variable_choices(
              ADSL,
              c(
                "ARM", "ACTARM", "SEX",
                "RACE", "ITTFL", "SAFFL", "STRATA2"
              )
            ),
            selected = NULL,
            multiple = FALSE
          )
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces a ggplot2::ggplot() type confidence interval plot consistent with the TLG Catalog template CIG01 available here.

Usage

tm_g_ci(
  label,
  x_var,
  y_var,
  color,
  stat = c("mean", "median"),
  conf_level = teal.transform::choices_selected(c(0.95, 0.9, 0.8), 0.95, keep_order =
    TRUE),
  plot_height = c(700L, 200L, 2000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

Arguments

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• plot (ggplot2)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

See Also

The TLG Catalog where additional example apps implementing this module can be found.

Examples

library(nestcolor)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADLB <- tmc_ex_adlb
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADLB <- data[["ADLB"]]

app <- init(
  data = data,
  modules = modules(
    tm_g_ci(
      label = "Confidence Interval Plot",
      x_var = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          choices = c("ARMCD", "BMRKR2"),
          selected = c("ARMCD"),
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      y_var = data_extract_spec(
        dataname = "ADLB",
        filter = list(
          filter_spec(
            vars = "PARAMCD",
            choices = levels(ADLB$PARAMCD),
            selected = levels(ADLB$PARAMCD)[1],
            multiple = FALSE,
            label = "Select lab:"
          ),
          filter_spec(
            vars = "AVISIT",
            choices = levels(ADLB$AVISIT),
            selected = levels(ADLB$AVISIT)[1],
            multiple = FALSE,
            label = "Select visit:"
          )
        ),
        select = select_spec(
          label = "Analyzed Value",
          choices = c("AVAL", "CHG"),
          selected = "AVAL",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      color = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Color by variable",
          choices = c("SEX", "STRATA1", "STRATA2"),
          selected = c("STRATA1"),
          multiple = FALSE,
          fixed = FALSE
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces a grid-style forest plot for response data with ADaM structure.

Usage

tm_g_forest_rsp(
  label,
  dataname,
  parentname = ifelse(inherits(arm_var, "data_extract_spec"),
    teal.transform::datanames_input(arm_var), "ADSL"),
  arm_var,
  arm_ref_comp = NULL,
  paramcd,
  aval_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AVALC"), "AVALC", fixed = TRUE),
  subgroup_var,
  strata_var,
  stats = c("n_tot", "n", "n_rsp", "prop", "or", "ci"),
  riskdiff = NULL,
  fixed_symbol_size = TRUE,
  conf_level = teal.transform::choices_selected(c(0.95, 0.9, 0.8), 0.95, keep_order =
    TRUE),
  default_responses = c("CR", "PR", "Y", "Complete Response (CR)",
    "Partial Response (PR)"),
  plot_height = c(500L, 200L, 2000L),
  plot_width = c(1500L, 800L, 3000L),
  rel_width_forest = c(25L, 0L, 100L),
  font_size = c(15L, 1L, 30L),
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

Arguments

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• plot (ggplot2)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

See Also

The TLG Catalog where additional example apps implementing this module can be found.

Examples

library(nestcolor)
library(dplyr)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADRS <- tmc_ex_adrs %>%
    mutate(AVALC = d_onco_rsp_label(AVALC) %>%
      with_label("Character Result/Finding")) %>%
    filter(PARAMCD != "OVRINV" | AVISIT == "FOLLOW UP")
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADRS <- data[["ADRS"]]

arm_ref_comp <- list(
  ARM = list(
    ref = "B: Placebo",
    comp = c("A: Drug X", "C: Combination")
  ),
  ARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  )
)

app <- init(
  data = data,
  modules = modules(
    tm_g_forest_rsp(
      label = "Forest Response",
      dataname = "ADRS",
      arm_var = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD")),
        "ARMCD"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(ADRS, "PARAMCD", "PARAM"),
        "INVET"
      ),
      subgroup_var = choices_selected(
        variable_choices(ADSL, names(ADSL)),
        c("BMRKR2", "SEX")
      ),
      strata_var = choices_selected(
        variable_choices(ADSL, c("STRATA1", "STRATA2")),
        "STRATA2"
      ),
      plot_height = c(600L, 200L, 2000L),
      default_responses = list(
        BESRSPI = list(
          rsp = c("Stable Disease (SD)", "Not Evaluable (NE)"),
          levels = c(
            "Complete Response (CR)", "Partial Response (PR)", "Stable Disease (SD)",
            "Progressive Disease (PD)", "Not Evaluable (NE)"
          )
        ),
        INVET = list(
          rsp = c("Complete Response (CR)", "Partial Response (PR)"),
          levels = c(
            "Complete Response (CR)", "Not Evaluable (NE)", "Partial Response (PR)",
            "Progressive Disease (PD)", "Stable Disease (SD)"
          )
        ),
        OVRINV = list(
          rsp = c("Progressive Disease (PD)", "Stable Disease (SD)"),
          levels = c("Progressive Disease (PD)", "Stable Disease (SD)", "Not Evaluable (NE)")
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces a grid-style forest plot for time-to-event data with ADaM structure.

Usage

tm_g_forest_tte(
  label,
  dataname,
  parentname = ifelse(inherits(arm_var, "data_extract_spec"),
    teal.transform::datanames_input(arm_var), "ADSL"),
  arm_var,
  arm_ref_comp = NULL,
  subgroup_var,
  paramcd,
  strata_var,
  aval_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AVAL"), "AVAL", fixed = TRUE),
  cnsr_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "CNSR"), "CNSR", fixed = TRUE),
  stats = c("n_tot_events", "n_events", "median", "hr", "ci"),
  riskdiff = NULL,
  conf_level = teal.transform::choices_selected(c(0.95, 0.9, 0.8), 0.95, keep_order =
    TRUE),
  time_unit_var =
    teal.transform::choices_selected(teal.transform::variable_choices(dataname, "AVALU"),
    "AVALU", fixed = TRUE),
  fixed_symbol_size = TRUE,
  plot_height = c(500L, 200L, 2000L),
  plot_width = c(1500L, 800L, 3000L),
  rel_width_forest = c(25L, 0L, 100L),
  font_size = c(15L, 1L, 30L),
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

Arguments

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• plot (ggplot2)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

See Also

The TLG Catalog where additional example apps implementing this module can be found.

Examples

library(nestcolor)
library(dplyr)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADTTE <- tmc_ex_adtte
  ADSL$RACE <- droplevels(ADSL$RACE) %>% with_label("Race")
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADTTE <- data[["ADTTE"]]

arm_ref_comp <- list(
  ARM = list(
    ref = "B: Placebo",
    comp = c("A: Drug X", "C: Combination")
  ),
  ARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  )
)

app <- init(
  data = data,
  modules = modules(
    tm_g_forest_tte(
      label = "Forest Survival",
      dataname = "ADTTE",
      arm_var = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD")),
        "ARMCD"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(ADTTE, "PARAMCD", "PARAM"),
        "OS"
      ),
      subgroup_var = choices_selected(
        variable_choices(ADSL, names(ADSL)),
        c("BMRKR2", "SEX")
      ),
      strata_var = choices_selected(
        variable_choices(ADSL, c("STRATA1", "STRATA2")),
        "STRATA2"
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces ggplot2::ggplot() type individual patient plots that display trends in parameter values over time for each patient, using data with ADaM structure.

Usage

tm_g_ipp(
  label,
  dataname,
  parentname = ifelse(inherits(arm_var, "data_extract_spec"),
    teal.transform::datanames_input(arm_var), "ADSL"),
  arm_var,
  paramcd,
  id_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "USUBJID"), "USUBJID", fixed = TRUE),
  visit_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AVISIT"), "AVISIT", fixed = TRUE),
  aval_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AVAL"), "AVAL", fixed = TRUE),
  avalu_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AVALU"), "AVALU", fixed = TRUE),
  base_var = lifecycle::deprecated(),
  baseline_var =
    teal.transform::choices_selected(teal.transform::variable_choices(dataname, "BASE"),
    "BASE", fixed = TRUE),
  add_baseline_hline = FALSE,
  separate_by_obs = FALSE,
  suppress_legend = FALSE,
  add_avalu = TRUE,
  plot_height = c(1200L, 400L, 5000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

Arguments

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• plot (ggplot2)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

See Also

The TLG Catalog where additional example apps implementing this module can be found.

Examples

library(nestcolor)
library(dplyr)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl %>%
    slice(1:20) %>%
    df_explicit_na()
  ADLB <- tmc_ex_adlb %>%
    filter(USUBJID %in% ADSL$USUBJID) %>%
    df_explicit_na() %>%
    filter(AVISIT != "SCREENING")
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADLB <- data[["ADLB"]]

app <- init(
  data = data,
  modules = modules(
    tm_g_ipp(
      label = "Individual Patient Plot",
      dataname = "ADLB",
      arm_var = choices_selected(
        value_choices(ADLB, "ARMCD"),
        "ARM A"
      ),
      paramcd = choices_selected(
        value_choices(ADLB, "PARAMCD"),
        "ALT"
      ),
      aval_var = choices_selected(
        variable_choices(ADLB, c("AVAL", "CHG")),
        "AVAL"
      ),
      avalu_var = choices_selected(
        variable_choices(ADLB, c("AVALU")),
        "AVALU",
        fixed = TRUE
      ),
      id_var = choices_selected(
        variable_choices(ADLB, c("USUBJID")),
        "USUBJID",
        fixed = TRUE
      ),
      visit_var = choices_selected(
        variable_choices(ADLB, c("AVISIT")),
        "AVISIT"
      ),
      baseline_var = choices_selected(
        variable_choices(ADLB, c("BASE")),
        "BASE",
        fixed = TRUE
      ),
      add_baseline_hline = FALSE,
      separate_by_obs = FALSE
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces a ggplot-style Kaplan-Meier plot for data with ADaM structure.

Usage

tm_g_km(
  label,
  dataname,
  parentname = ifelse(inherits(arm_var, "data_extract_spec"),
    teal.transform::datanames_input(arm_var), "ADSL"),
  arm_var,
  arm_ref_comp = NULL,
  paramcd,
  strata_var,
  facet_var,
  time_unit_var =
    teal.transform::choices_selected(teal.transform::variable_choices(dataname, "AVALU"),
    "AVALU", fixed = TRUE),
  aval_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AVAL"), "AVAL", fixed = TRUE),
  cnsr_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "CNSR"), "CNSR", fixed = TRUE),
  conf_level = teal.transform::choices_selected(c(0.95, 0.9, 0.8), 0.95, keep_order =
    TRUE),
  font_size = c(11L, 1L, 30),
  control_annot_surv_med = control_surv_med_annot(),
  control_annot_coxph = control_coxph_annot(x = 0.27, y = 0.35, w = 0.3),
  legend_pos = c(0.9, 0.5),
  rel_height_plot = c(80L, 0L, 100L),
  plot_height = c(800L, 400L, 5000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  decorators = NULL
)

Arguments

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• plot (ggplot2)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

See Also

The TLG Catalog where additional example apps implementing this module can be found.

Examples

library(nestcolor)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADTTE <- tmc_ex_adtte
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADTTE <- data[["ADTTE"]]

arm_ref_comp <- list(
  ACTARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  ),
  ARM = list(
    ref = "B: Placebo",
    comp = c("A: Drug X", "C: Combination")
  )
)

app <- init(
  data = data,
  modules = modules(
    tm_g_km(
      label = "Kaplan-Meier Plot",
      dataname = "ADTTE",
      arm_var = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD", "ACTARMCD")),
        "ARM"
      ),
      paramcd = choices_selected(
        value_choices(ADTTE, "PARAMCD", "PARAM"),
        "OS"
      ),
      arm_ref_comp = arm_ref_comp,
      strata_var = choices_selected(
        variable_choices(ADSL, c("SEX", "BMRKR2")),
        "SEX"
      ),
      facet_var = choices_selected(
        variable_choices(ADSL, c("SEX", "BMRKR2")),
        NULL
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces a ggplot2::ggplot() type line plot, with optional summary table, for standard ADaM data.

Usage

tm_g_lineplot(
  label,
  dataname,
  parentname = NULL,
  strata = lifecycle::deprecated(),
  group_var =
    teal.transform::choices_selected(teal.transform::variable_choices(parentname,
    c("ARM", "ARMCD", "ACTARMCD")), "ARM"),
  x = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AVISIT"), "AVISIT", fixed = TRUE),
  y = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    c("AVAL", "BASE", "CHG", "PCHG")), "AVAL"),
  y_unit = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AVALU"), "AVALU", fixed = TRUE),
  paramcd = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "PARAMCD"), "PARAMCD", fixed = TRUE),
  param = teal.transform::choices_selected(teal.transform::value_choices(dataname,
    "PARAMCD", "PARAM"), "ALT"),
  conf_level = teal.transform::choices_selected(c(0.95, 0.9, 0.8), 0.95, keep_order =
    TRUE),
  interval = "mean_ci",
  mid = "mean",
  whiskers = c("mean_ci_lwr", "mean_ci_upr"),
  table = c("n", "mean_sd", "median", "range"),
  mid_type = "pl",
  mid_point_size = c(2, 1, 5),
  table_font_size = c(4, 2, 6),
  plot_height = c(1000L, 200L, 4000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

Arguments

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• plot (ggplot2)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

See Also

The TLG Catalog where additional example apps implementing this module can be found.

Examples

library(nestcolor)
library(dplyr)
library(forcats)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADLB <- tmc_ex_adlb %>%
    mutate(AVISIT == fct_reorder(AVISIT, AVISITN, min))
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADLB <- data[["ADLB"]]

app <- init(
  data = data,
  modules = modules(
    tm_g_lineplot(
      label = "Line Plot",
      dataname = "ADLB",
      group_var = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD", "ACTARMCD")),
        "ARM"
      ),
      y = choices_selected(
        variable_choices(ADLB, c("AVAL", "BASE", "CHG", "PCHG")),
        "AVAL"
      ),
      param = choices_selected(
        value_choices(ADLB, "PARAMCD", "PARAM"),
        "ALT"
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces an adverse events table and ggplot2::ggplot() type plot using ADaM datasets.

Usage

tm_g_pp_adverse_events(
  label,
  dataname = "ADAE",
  parentname = "ADSL",
  patient_col = "USUBJID",
  aeterm = NULL,
  tox_grade = NULL,
  causality = NULL,
  outcome = NULL,
  action = NULL,
  time = NULL,
  decod = NULL,
  font_size = c(12L, 12L, 25L),
  plot_height = c(700L, 200L, 2000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

Arguments

Value

a teal_module object.

Decorating Modules

This module generates the following objects, which can be modified in place using decorators::

• plot (ggplot2)

• table (listing_df - output of rlistings::as_listing)

Decorators can be applied to all outputs or only to specific objects using a named list of teal_transform_module objects. The "default" name is reserved for decorators that are applied to all outputs. See code snippet below:

tm_g_pp_adverse_events(
   ..., # arguments for module
   decorators = list(
     default = list(teal_transform_module(...)), # applied to all outputs
     plot = list(teal_transform_module(...)), # applied only to `plot` output
     table = list(teal_transform_module(...)) # applied only to `table` output
   )
)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

Examples

library(nestcolor)
library(dplyr)

data <- teal_data()
data <- within(data, {
  ADAE <- tmc_ex_adae
  ADSL <- tmc_ex_adsl %>%
    filter(USUBJID %in% ADAE$USUBJID)
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADAE <- data[["ADAE"]]

app <- init(
  data = data,
  modules = modules(
    tm_g_pp_adverse_events(
      label = "Adverse Events",
      dataname = "ADAE",
      parentname = "ADSL",
      patient_col = "USUBJID",
      plot_height = c(600L, 200L, 2000L),
      aeterm = choices_selected(
        choices = variable_choices(ADAE, "AETERM"),
        selected = "AETERM"
      ),
      tox_grade = choices_selected(
        choices = variable_choices(ADAE, "AETOXGR"),
        selected = "AETOXGR"
      ),
      causality = choices_selected(
        choices = variable_choices(ADAE, "AEREL"),
        selected = "AEREL"
      ),
      outcome = choices_selected(
        choices = variable_choices(ADAE, "AEOUT"),
        selected = "AEOUT"
      ),
      action = choices_selected(
        choices = variable_choices(ADAE, "AEACN"),
        selected = "AEACN"
      ),
      time = choices_selected(
        choices = variable_choices(ADAE, "ASTDY"),
        selected = "ASTDY"
      ),
      decod = NULL
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces a patient profile timeline ggplot2::ggplot() type plot using ADaM datasets.

Usage

tm_g_pp_patient_timeline(
  label,
  dataname_adcm = "ADCM",
  dataname_adae = "ADAE",
  parentname = "ADSL",
  patient_col = "USUBJID",
  aeterm = NULL,
  cmdecod = NULL,
  aetime_start = NULL,
  aetime_end = NULL,
  dstime_start = NULL,
  dstime_end = NULL,
  aerelday_start = NULL,
  aerelday_end = NULL,
  dsrelday_start = NULL,
  dsrelday_end = NULL,
  font_size = c(12L, 12L, 25L),
  plot_height = c(700L, 200L, 2000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

Arguments

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• plot (ggplot2)

For additional details and examples of decorators, refer to the vignette vignette("decorate-modules-output", package = "teal") or the teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

Examples

library(nestcolor)
library(dplyr)

data <- teal_data()
data <- within(data, {
  ADAE <- tmc_ex_adae
  ADSL <- tmc_ex_adsl %>%
    filter(USUBJID %in% ADAE$USUBJID)
  ADCM <- tmc_ex_adcm %>%
    mutate(
      CMSTDY = case_when(
        CMCAT == "medcl B" ~ 20,
        CMCAT == "medcl C" ~ 150,
        TRUE ~ 1
      ) %>% with_label("Study Day of Start of Medication"),
      CMENDY = case_when(
        CMCAT == "medcl B" ~ 700,
        CMCAT == "medcl C" ~ 1000,
        TRUE ~ 500
      ) %>% with_label("Study Day of End of Medication"),
      CMASTDTM = ASTDTM,
      CMAENDTM = AENDTM
    )
})

join_keys(data) <- default_cdisc_join_keys[c("ADSL", "ADAE", "ADCM")]
adcm_keys <- c("STUDYID", "USUBJID", "ASTDTM", "CMSEQ", "ATC1", "ATC2", "ATC3", "ATC4")
join_keys(data)["ADCM", "ADCM"] <- adcm_keys
join_keys(data)["ADAE", "ADCM"] <- c("STUDYID", "USUBJID")

app <- init(
  data = data,
  modules = modules(
    tm_g_pp_patient_timeline(
      label = "Patient Timeline",
      dataname_adae = "ADAE",
      dataname_adcm = "ADCM",
      parentname = "ADSL",
      patient_col = "USUBJID",
      plot_height = c(600L, 200L, 2000L),
      cmdecod = choices_selected(
        choices = variable_choices(data[["ADCM"]], "CMDECOD"),
        selected = "CMDECOD",
      ),
      aeterm = choices_selected(
        choices = variable_choices(data[["ADAE"]], "AETERM"),
        selected = c("AETERM")
      ),
      aetime_start = choices_selected(
        choices = variable_choices(data[["ADAE"]], "ASTDTM"),
        selected = c("ASTDTM")
      ),
      aetime_end = choices_selected(
        choices = variable_choices(data[["ADAE"]], "AENDTM"),
        selected = c("AENDTM")
      ),
      dstime_start = choices_selected(
        choices = variable_choices(data[["ADCM"]], "CMASTDTM"),
        selected = c("CMASTDTM")
      ),
      dstime_end = choices_selected(
        choices = variable_choices(data[["ADCM"]], "CMAENDTM"),
        selected = c("CMAENDTM")
      ),
      aerelday_start = choices_selected(
        choices = variable_choices(data[["ADAE"]], "ASTDY"),
        selected = c("ASTDY")
      ),
      aerelday_end = choices_selected(
        choices = variable_choices(data[["ADAE"]], "AENDY"),
        selected = c("AENDY")
      ),
      dsrelday_start = choices_selected(
        choices = variable_choices(data[["ADCM"]], "ASTDY"),
        selected = c("ASTDY")
      ),
      dsrelday_end = choices_selected(
        choices = variable_choices(data[["ADCM"]], "AENDY"),
        selected = c("AENDY")
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

Description

This module produces a patient profile therapy table and ggplot2::ggplot() type plot using ADaM datasets.

Usage

tm_g_pp_therapy(
  label,
  dataname = "ADCM",
  parentname = "ADSL",
  patient_col = "USUBJID",
  atirel = NULL,
  cmdecod = NULL,
  cmindc = NULL,
  cmdose = NULL,
  cmtrt = NULL,
  cmdosu = NULL,
  cmroute = NULL,
  cmdosfrq = NULL,
  cmstdy = NULL,
  cmendy = NULL,
  font_size = c(12L, 12L, 25L),
  plot_height = c(700L, 200L, 2000L),
  plot_width = NULL,
  pre_output = NULL,
  post_output = NULL,
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

Arguments