Package 'admiral'

Description

See magrittr::%>% for more details.

Usage

lhs %>% rhs

Arguments

Description

An example of lab analysis dataset

Usage

admiral_adlb

Format

An object of class tbl_df (inherits from tbl, data.frame) with 3779 rows and 111 columns.

Source

Derived from the adlb template, then further filtered due to dataset size by the following USUBJIDs: 01-701-1015, 01-701-1023, 01-701-1028, 01-701-1033, 01-701-1034, 01-701-1047, 01-701-1097, 01-705-1186, 01-705-1292, 01-705-1310, 01-708-1286

See Also

Other datasets: admiral_adsl, ex_single, example_qs, queries, queries_mh

Description

An example subject level analysis dataset

Usage

admiral_adsl

Format

An object of class tbl_df (inherits from tbl, data.frame) with 306 rows and 54 columns.

Source

Derived from the dm and ds datasets using {admiral} (https://github.com/pharmaverse/admiral/blob/main/inst/templates/ad_adsl.R)

See Also

Other datasets: admiral_adlb, ex_single, example_qs, queries, queries_mh

Description

Metadata Holding Grading Criteria for NCI-CTCAEv4

Usage

atoxgr_criteria_ctcv4

Format

An object of class tbl_df (inherits from tbl, data.frame) with 40 rows and 13 columns.

Details

This metadata has its origin in the ADLB Grading Spec Excel file which ships with {admiral} and can be accessed using system.file("adlb_grading/adlb_grading_spec.xlsx", package = "admiral") in sheet = "NCICTCAEv4". The dataset contained in there has the following columns:

• SOC: variable to hold the SOC of the lab test criteria.

• TERM: variable to hold the term describing the criteria applied to a particular lab test, eg. 'Anemia' or 'INR Increased'. Note: the variable is case insensitive.

• ⁠Grade 1⁠: Criteria defining lab value as Grade 1.

• ⁠Grade 2⁠: Criteria defining lab value as Grade 2.

• ⁠Grade 3⁠: Criteria defining lab value as Grade 3.

• ⁠Grade 4⁠: Criteria defining lab value as Grade 4.

• ⁠Grade 5⁠: Criteria defining lab value as Grade 5.

• Definition: Holds the definition of the lab test abnormality.

• GRADE_CRITERIA_CODE: variable to hold code that creates grade based on defined criteria.

• SI_UNIT_CHECK: variable to hold unit of particular lab test. Used to check against input data if criteria is based on absolute values.

• VAR_CHECK: List of variables required to implement lab grade criteria. Use to check against input data.

• DIRECTION: variable to hold the direction of the abnormality of a particular lab test value. 'L' is for LOW values, 'H' is for HIGH values. Note: the variable is case insensitive.

• COMMENT: Holds any information regarding rationale behind implementation of grading criteria.

Note: Variables SOC, TERM, ⁠Grade 1⁠, ⁠Grade 2⁠,⁠Grade 3⁠,⁠Grade 4⁠,⁠Grade 5⁠, Definition are from the source document on NCI-CTC website defining the grading criteria. Common Terminology Criteria for Adverse Events (CTCAE)v4.0 From these variables only 'TERM' is used in the {admiral} code, the rest are for information and traceability only.

See Also

Other metadata: atoxgr_criteria_ctcv5, atoxgr_criteria_daids, country_code_lookup, dose_freq_lookup

Description

Metadata Holding Grading Criteria for NCI-CTCAEv5

Usage

atoxgr_criteria_ctcv5

Format

An object of class tbl_df (inherits from tbl, data.frame) with 37 rows and 13 columns.

Details

This metadata has its origin in the ADLB Grading Spec Excel file which ships with {admiral} and can be accessed using system.file("adlb_grading/adlb_grading_spec.xlsx", package = "admiral") in sheet = "NCICTCAEv5". The dataset contained in there has the following columns:

• SOC: variable to hold the SOC of the lab test criteria.

• TERM: variable to hold the term describing the criteria applied to a particular lab test, eg. 'Anemia' or 'INR Increased'. Note: the variable is case insensitive.

• ⁠Grade 1⁠: Criteria defining lab value as Grade 1.

• ⁠Grade 2⁠: Criteria defining lab value as Grade 2.

• ⁠Grade 3⁠: Criteria defining lab value as Grade 3.

• ⁠Grade 4⁠: Criteria defining lab value as Grade 4.

• ⁠Grade 5⁠: Criteria defining lab value as Grade 5.

• Definition: Holds the definition of the lab test abnormality.

• GRADE_CRITERIA_CODE: variable to hold code that creates grade based on defined criteria.

• SI_UNIT_CHECK: variable to hold unit of particular lab test. Used to check against input data if criteria is based on absolute values.

• VAR_CHECK: List of variables required to implement lab grade criteria. Use to check against input data.

• DIRECTION: variable to hold the direction of the abnormality of a particular lab test value. 'L' is for LOW values, 'H' is for HIGH values. Note: the variable is case insensitive.

• COMMENT: Holds any information regarding rationale behind implementation of grading criteria.

Note: Variables SOC, TERM, ⁠Grade 1⁠, ⁠Grade 2⁠,⁠Grade 3⁠,⁠Grade 4⁠,⁠Grade 5⁠, Definition are from the source document on NCI-CTC website defining the grading criteria. Common Terminology Criteria for Adverse Events (CTCAE)v5.0 From these variables only 'TERM' is used in the {admiral} code, the rest are for information and traceability only.

See Also

Other metadata: atoxgr_criteria_ctcv4, atoxgr_criteria_daids, country_code_lookup, dose_freq_lookup

Description

Metadata Holding Grading Criteria for DAIDs

Usage

atoxgr_criteria_daids

Format

An object of class tbl_df (inherits from tbl, data.frame) with 63 rows and 15 columns.

Details

This metadata has its origin in the ADLB Grading Spec Excel file which ships with {admiral} and can be accessed using system.file("adlb_grading/adlb_grading_spec.xlsx", package = "admiral") in sheet = "DAIDS". The dataset contained in there has the following columns:

• SOC: variable to hold the SOC of the lab test criteria.

• TERM: variable to hold the term describing the criteria applied to a particular lab test, eg. 'Anemia' or 'INR Increased'. Note: the variable is case insensitive.

• SUBGROUP : Description of sub-group of subjects were grading will be applied (i.e. >= 18 years)

• ⁠Grade 1⁠: Criteria defining lab value as Grade 1.

• ⁠Grade 2⁠: Criteria defining lab value as Grade 2.

• ⁠Grade 3⁠: Criteria defining lab value as Grade 3.

• ⁠Grade 4⁠: Criteria defining lab value as Grade 4.

• ⁠Grade 5⁠: Criteria defining lab value as Grade 5.

• Definition: Holds the definition of the lab test abnormality.

• FILTER : admiral code to apply the filter based on SUBGROUP column.

• GRADE_CRITERIA_CODE: variable to hold code that creates grade based on defined criteria.

• SI_UNIT_CHECK: variable to hold unit of particular lab test. Used to check against input data if criteria is based on absolute values.

• VAR_CHECK: List of variables required to implement lab grade criteria. Use to check against input data.

• DIRECTION: variable to hold the direction of the abnormality of a particular lab test value. 'L' is for LOW values, 'H' is for HIGH values. Note: the variable is case insensitive.

• COMMENT: Holds any information regarding rationale behind implementation of grading criteria.

Note: Variables SOC, TERM, SUBGROUP, ⁠Grade 1⁠, ⁠Grade 2⁠,⁠Grade 3⁠,⁠Grade 4⁠,⁠Grade 5⁠, Definition are from the source document on DAIDS website defining the grading criteria. [Division of AIDS (DAIDS) Table for Grading the Severity of Adult and Pediatric Adverse Events From these variables only 'TERM' is used in the {admiral} code, the rest are for information and traceability only.

See Also

Other metadata: atoxgr_criteria_ctcv4, atoxgr_criteria_ctcv5, country_code_lookup, dose_freq_lookup

Description

Create a basket_select object

Usage

basket_select(name = NULL, id = NULL, scope = NULL, type, ...)

Arguments

Details

Exactly one of name or id must be specified.

Value

An object of class basket_select.

See Also

create_query_data(), query()

Source Objects: censor_source(), death_event, event(), event_joined(), event_source(), flag_event(), query(), records_source(), tte_source()

Description

Call a single derivation multiple times with some parameters/arguments being fixed across iterations and others varying.

Usage

call_derivation(dataset = NULL, derivation, variable_params, ...)

Arguments

Value

The input dataset with additional records/variables added depending on which derivation has been used.

See Also

params()

Higher Order Functions: derivation_slice(), restrict_derivation(), slice_derivation()

Examples

library(dplyr, warn.conflicts = FALSE)
adsl <- tribble(
  ~STUDYID,   ~USUBJID,      ~TRTSDT,      ~TRTEDT,
  "PILOT01", "01-1307",           NA,           NA,
  "PILOT01", "05-1377", "2014-01-04", "2014-01-25",
  "PILOT01", "06-1384", "2012-09-15", "2012-09-24",
  "PILOT01", "15-1085", "2013-02-16", "2013-08-18",
  "PILOT01", "16-1298", "2013-04-08", "2013-06-28"
) %>%
  mutate(
    across(TRTSDT:TRTEDT, as.Date)
  )

ae <- tribble(
  ~STUDYID,  ~DOMAIN,  ~USUBJID,     ~AESTDTC,     ~AEENDTC,
  "PILOT01",    "AE", "06-1384", "2012-09-15", "2012-09-29",
  "PILOT01",    "AE", "06-1384", "2012-09-15", "2012-09-29",
  "PILOT01",    "AE", "06-1384", "2012-09-23", "2012-09-29",
  "PILOT01",    "AE", "06-1384", "2012-09-23", "2012-09-29",
  "PILOT01",    "AE", "06-1384", "2012-09-15", "2012-09-29",
  "PILOT01",    "AE", "06-1384", "2012-09-15", "2012-09-29",
  "PILOT01",    "AE", "06-1384", "2012-09-15", "2012-09-29",
  "PILOT01",    "AE", "06-1384", "2012-09-15", "2012-09-29",
  "PILOT01",    "AE", "06-1384", "2012-09-23", "2012-09-29",
  "PILOT01",    "AE", "06-1384", "2012-09-23", "2012-09-29",
  "PILOT01",    "AE", "16-1298", "2013-06-08", "2013-07-06",
  "PILOT01",    "AE", "16-1298", "2013-06-08", "2013-07-06",
  "PILOT01",    "AE", "16-1298", "2013-04-22", "2013-07-06",
  "PILOT01",    "AE", "16-1298", "2013-04-22", "2013-07-06",
  "PILOT01",    "AE", "16-1298", "2013-04-22", "2013-07-06",
  "PILOT01",    "AE", "16-1298", "2013-04-22", "2013-07-06"
)

adae <- ae %>%
  derive_vars_merged(
    dataset_add = adsl,
    new_vars = exprs(TRTSDT, TRTEDT),
    by_vars = exprs(USUBJID)
  )

## While `derive_vars_dt()` can only add one variable at a time, using `call_derivation()`
## one can add multiple variables in one go
call_derivation(
  dataset = adae,
  derivation = derive_vars_dt,
  variable_params = list(
    params(dtc = AESTDTC, date_imputation = "first", new_vars_prefix = "AST"),
    params(dtc = AEENDTC, date_imputation = "last", new_vars_prefix = "AEN")
  ),
  min_dates = exprs(TRTSDT),
  max_dates = exprs(TRTEDT)
)

## The above call using `call_derivation()` is equivalent to the following
adae %>%
  derive_vars_dt(
    new_vars_prefix = "AST",
    dtc = AESTDTC,
    date_imputation = "first",
    min_dates = exprs(TRTSDT),
    max_dates = exprs(TRTEDT)
  ) %>%
  derive_vars_dt(
    new_vars_prefix = "AEN",
    dtc = AEENDTC,
    date_imputation = "last",
    min_dates = exprs(TRTSDT),
    max_dates = exprs(TRTEDT)
  )

Description

Calls a function provided by the user and adds the function call to the error message if the call fails.

Usage

call_user_fun(call)

Arguments

Value

The return value of the function call

See Also

Utilities used within Derivation functions: extract_unit(), get_flagged_records(), get_not_mapped(), get_vars_query()

Examples

call_user_fun(compute_bmi(
  height = 172,
  weight = 60
))

try(call_user_fun(compute_bmi(
  height = 172,
  weight = "hallo"
)))

Description

censor_source objects are used to define censorings as input for the derive_param_tte() function.

Note: This is a wrapper function for the more generic tte_source().

Usage

censor_source(
  dataset_name,
  filter = NULL,
  date,
  censor = 1,
  set_values_to = NULL
)

Arguments

Value

An object of class censor_source, inheriting from class tte_source

See Also

derive_param_tte(), event_source()

Source Objects: basket_select(), death_event, event(), event_joined(), event_source(), flag_event(), query(), records_source(), tte_source()

Examples

# Last study date known alive censor

censor_source(
  dataset_name = "adsl",
  date = LSTALVDT,
  set_values_to = exprs(
    EVNTDESC = "ALIVE",
    SRCDOM = "ADSL",
    SRCVAR = "LSTALVDT"
  )
)

Description

Turn a character vector into a list of expressions

Usage

chr2vars(chr)

Arguments

Value

A list of expressions as returned by exprs()

See Also

Utilities for working with quosures/list of expressions: negate_vars()

Examples

chr2vars(c("USUBJID", "AVAL"))

Description

Converts a set of age values from the specified time unit to years.

Usage

compute_age_years(age, age_unit)

Arguments

Details

Returns a numeric vector of ages in years as doubles. Note that passing NA_character_ as a unit will result in an NA value for the outputted age. Also note, underlying computations assume an equal number of days in each year (365.25).

Value

The ages contained in age converted to years.

See Also

Date/Time Computation Functions that returns a vector: compute_dtf(), compute_duration(), compute_tmf(), convert_date_to_dtm(), convert_dtc_to_dt(), convert_dtc_to_dtm(), impute_dtc_dt(), impute_dtc_dtm()

Examples

compute_age_years(
  age = c(240, 360, 480),
  age_unit = "MONTHS"
)

compute_age_years(
  age = c(10, 520, 3650, 1000),
  age_unit = c("YEARS", "WEEKS", "DAYS", NA_character_)
)

Description

Computes BMI from height and weight

Usage

compute_bmi(height, weight)

Arguments

Details

Usually this computation function can not be used with ⁠%>%⁠.

Value

The BMI (Body Mass Index Area) in kg/m^2.

See Also

derive_param_bmi()

BDS-Findings Functions that returns a vector: compute_bsa(), compute_egfr(), compute_framingham(), compute_map(), compute_qtc(), compute_qual_imputation(), compute_qual_imputation_dec(), compute_rr(), compute_scale()

Examples

compute_bmi(height = 170, weight = 75)

Description

Computes BSA from height and weight making use of the specified derivation method

Usage

compute_bsa(height = height, weight = weight, method)

Arguments

Details

Usually this computation function can not be used with ⁠%>%⁠.

Value

The BSA (Body Surface Area) in m^2.

See Also

derive_param_bsa()

BDS-Findings Functions that returns a vector: compute_bmi(), compute_egfr(), compute_framingham(), compute_map(), compute_qtc(), compute_qual_imputation(), compute_qual_imputation_dec(), compute_rr(), compute_scale()

Examples

# Derive BSA by the Mosteller method
compute_bsa(
  height = 170,
  weight = 75,
  method = "Mosteller"
)

# Derive BSA by the DuBois & DuBois method
compute_bsa(
  height = c(170, 185),
  weight = c(75, 90),
  method = "DuBois-DuBois"
)

Description

Derive the date imputation flag ('--DTF') comparing a date character vector ('--DTC') with a Date vector ('--DT').

Usage

compute_dtf(dtc, dt)

Arguments

Details

Usually this computation function can not be used with ⁠%>%⁠.

Value

The date imputation flag ('--DTF') (character value of 'D', 'M' , 'Y' or NA)

See Also

Date/Time Computation Functions that returns a vector: compute_age_years(), compute_duration(), compute_tmf(), convert_date_to_dtm(), convert_dtc_to_dt(), convert_dtc_to_dtm(), impute_dtc_dt(), impute_dtc_dtm()

Examples

compute_dtf(dtc = "2019-07", dt = as.Date("2019-07-18"))
compute_dtf(dtc = "2019", dt = as.Date("2019-07-18"))
compute_dtf(dtc = "--06-01T00:00", dt = as.Date("2022-06-01"))
compute_dtf(dtc = "2022-06--T00:00", dt = as.Date("2022-06-01"))
compute_dtf(dtc = "2022---01T00:00", dt = as.Date("2022-06-01"))
compute_dtf(dtc = "2022----T00:00", dt = as.Date("2022-06-01"))

Description

Compute duration between two dates, e.g., duration of an adverse event, relative day, age, ...

Usage

compute_duration(
  start_date,
  end_date,
  in_unit = "days",
  out_unit = "days",
  floor_in = TRUE,
  add_one = TRUE,
  trunc_out = FALSE,
  type = "duration"
)

Arguments

Details

The output is a numeric vector providing the duration as time from start to end date in the specified unit. If the end date is before the start date, the duration is negative.

Value

The duration between the two date in the specified unit

Duration Type

The lubridate package calculates two types of spans between two dates: duration and interval. While these calculations are largely the same, when the unit of the time period is month or year the result can be slightly different.

The difference arises from the ambiguity in the length of "1 month" or "1 year". Months may have 31, 30, 28, or 29 days, and years are 365 days and 366 during leap years. Durations and intervals help solve the ambiguity in these measures.

The interval between 2000-02-01 and 2000-03-01 is 1 (i.e. one month). The duration between these two dates is 0.95, which accounts for the fact that the year 2000 is a leap year, February has 29 days, and the average month length is 30.4375, i.e. 29 / 30.4375 = 0.95.

For additional details, review the lubridate time span reference page.

See Also

derive_vars_duration()

Date/Time Computation Functions that returns a vector: compute_age_years(), compute_dtf(), compute_tmf(), convert_date_to_dtm(), convert_dtc_to_dt(), convert_dtc_to_dtm(), impute_dtc_dt(), impute_dtc_dtm()

Examples

library(lubridate)

# Derive duration in days (integer), i.e., relative day
compute_duration(
  start_date = ymd_hms("2020-12-06T15:00:00"),
  end_date = ymd_hms("2020-12-24T08:15:00")
)

# Derive duration in days (float)
compute_duration(
  start_date = ymd_hms("2020-12-06T15:00:00"),
  end_date = ymd_hms("2020-12-24T08:15:00"),
  floor_in = FALSE,
  add_one = FALSE
)

# Derive age in years
compute_duration(
  start_date = ymd("1984-09-06"),
  end_date = ymd("2020-02-24"),
  trunc_out = TRUE,
  out_unit = "years",
  add_one = FALSE
)

# Derive duration in hours
compute_duration(
  start_date = ymd_hms("2020-12-06T9:00:00"),
  end_date = ymd_hms("2020-12-06T13:30:00"),
  out_unit = "hours",
  floor_in = FALSE,
  add_one = FALSE,
)

Description

Compute Kidney Function Tests:

• Estimated Creatinine Clearance (CRCL) by Cockcroft-Gault equation

• Estimated Glomerular Filtration Rate (eGFR) by CKD-EPI or MDRD equations

Usage

compute_egfr(creat, creatu = "SI", age, weight, sex, race = NULL, method)

Arguments

Details

Calculates an estimate of Glomerular Filtration Rate (eGFR)

CRCL Creatinine Clearance (Cockcroft-Gault)

For Creatinine in umol/L:

$\frac{(140 - age) \times weight(kg) \times constant}{Serum\:Creatinine(\mu mol/L)}$

$Constant = 1.04\:for\:females, 1.23\:for\:males$

For Creatinine in mg/dL:

$\frac{(140 - age) \times weight(kg) \times (0.85\:if\:female)}{72 \times Serum\:Creatinine(mg/dL)}$

units = mL/min

CKD-EPI Chronic Kidney Disease Epidemiology Collaboration formula

$eGFR = 142 \times min(SCr/{\kappa}, 1)^{\alpha} \times max(SCr/{\kappa}, 1)^{-1.200} \times 0.9938^{Age} \times 1.012 [if\:female]$

SCr = standardized serum creatinine in mg/dL (Note SCr(mg/dL) = Creat(umol/L) / 88.42)

$\kappa$

= 0.7 (females) or 0.9 (males)

$\alpha$

= -0.241 (female) or -0.302 (male) units = mL/min/1.73 m2

MDRD Modification of Diet in Renal Disease formula

$eGFR = 175 \times (SCr)^{-1.154} \times (age)^{-0.203} \times 0.742 [if\:female] \times 1.212 [if\:Black]$

SCr = standardized serum creatinine in mg/dL (Note SCr(mg/dL) = Creat(umol/L) / 88.42)

units = mL/min/1.73 m2

Value

A numeric vector of egfr values

See Also

BDS-Findings Functions that returns a vector: compute_bmi(), compute_bsa(), compute_framingham(), compute_map(), compute_qtc(), compute_qual_imputation(), compute_qual_imputation_dec(), compute_rr(), compute_scale()

Examples

compute_egfr(
  creat = 90, creatu = "umol/L", age = 53, weight = 85, sex = "M", method = "CRCL"
)

compute_egfr(
  creat = 90, creatu = "umol/L", age = 53, sex = "M", race = "ASIAN", method = "MDRD"
)

compute_egfr(
  creat = 70, creatu = "umol/L", age = 52, sex = "F", race = "BLACK OR AFRICAN AMERICAN",
  method = "MDRD"
)

compute_egfr(
  creat = 90, creatu = "umol/L", age = 53, sex = "M", method = "CKD-EPI"
)


base <- tibble::tribble(
  ~STUDYID, ~USUBJID, ~AGE, ~SEX, ~RACE, ~WTBL, ~CREATBL, ~CREATBLU,
  "P01", "P01-1001", 55, "M", "WHITE", 90.7, 96.3, "umol/L",
  "P01", "P01-1002", 52, "F", "BLACK OR AFRICAN AMERICAN", 68.5, 70, "umol/L",
  "P01", "P01-1003", 67, "M", "BLACK OR AFRICAN AMERICAN", 85.0, 77, "umol/L",
  "P01", "P01-1004", 76, "F", "ASIAN", 60.7, 65, "umol/L",
)

base %>%
  dplyr::mutate(
    CRCL_CG = compute_egfr(
      creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,
      method = "CRCL"
    ),
    EGFR_EPI = compute_egfr(
      creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,
      method = "CKD-EPI"
    ),
    EGFR_MDRD = compute_egfr(
      creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,
      race = RACE, method = "MDRD"
    ),
  )

Description

Computes Framingham Heart Study Cardiovascular Disease 10-Year Risk Score (FCVD101) based on systolic blood pressure, total serum cholesterol (mg/dL), HDL serum cholesterol (mg/dL), sex, smoking status, diabetic status, and treated for hypertension flag.

Usage

compute_framingham(sysbp, chol, cholhdl, age, sex, smokefl, diabetfl, trthypfl)

Arguments

Details

The predicted probability of having cardiovascular disease (CVD) within 10-years according to Framingham formula. See AHA Journal article General Cardiovascular Risk Profile for Use in Primary Care for reference.

For Women:

For Men:

The equation for calculating risk:

$RiskFactors = (log(Age) * AgeFactor) + (log(TotalChol) * TotalCholFactor) + (log(CholHDL) * CholHDLFactor) \\ + (log(SysBP) * SysBPFactor) + Smoker + Diabetes Present - AvgRisk$

$Risk = 100 * (1 - RiskPeriodFactor ^ exp(RiskFactors))$

Value

A numeric vector of Framingham values

See Also

derive_param_framingham()

BDS-Findings Functions that returns a vector: compute_bmi(), compute_bsa(), compute_egfr(), compute_map(), compute_qtc(), compute_qual_imputation(), compute_qual_imputation_dec(), compute_rr(), compute_scale()

Examples

compute_framingham(
  sysbp = 133, chol = 216.16, cholhdl = 54.91, age = 53,
  sex = "M", smokefl = "N", diabetfl = "N", trthypfl = "N"
)

compute_framingham(
  sysbp = 161, chol = 186.39, cholhdl = 64.19, age = 52,
  sex = "F", smokefl = "Y", diabetfl = "N", trthypfl = "Y"
)

Description

Computes mean arterial pressure (MAP) based on diastolic and systolic blood pressure. Optionally heart rate can be used as well.

Usage

compute_map(diabp, sysbp, hr = NULL)

Arguments

Details

$\frac{2DIABP + SYSBP}{3}$

if it is based on diastolic and systolic blood pressure and

$DIABP + 0.01 e^{4.14 - \frac{40.74}{HR}} (SYSBP - DIABP)$

if it is based on diastolic, systolic blood pressure, and heart rate.

Usually this computation function can not be used with ⁠%>%⁠.

Value

A numeric vector of MAP values

See Also

derive_param_map()

BDS-Findings Functions that returns a vector: compute_bmi(), compute_bsa(), compute_egfr(), compute_framingham(), compute_qtc(), compute_qual_imputation(), compute_qual_imputation_dec(), compute_rr(), compute_scale()

Examples

# Compute MAP based on diastolic and systolic blood pressure
compute_map(diabp = 51, sysbp = 121)

# Compute MAP based on diastolic and systolic blood pressure and heart rate
compute_map(diabp = 51, sysbp = 121, hr = 59)

Description

Computes corrected QT using Bazett's, Fridericia's or Sagie's formula.

Usage

compute_qtc(qt, rr, method)

Arguments

Details

Depending on the chosen method one of the following formulae is used.

Bazett:

$\frac{QT}{\sqrt{\frac{RR}{1000}}}$

Fridericia:

$\frac{QT}{\sqrt[3]{\frac{RR}{1000}}}$

Sagie:

$1000\left(\frac{QT}{1000} + 0.154\left(1 - \frac{RR}{1000}\right)\right)$

Usually this computation function can not be used with ⁠%>%⁠.

Value

QT interval in ms

See Also

derive_param_qtc()

BDS-Findings Functions that returns a vector: compute_bmi(), compute_bsa(), compute_egfr(), compute_framingham(), compute_map(), compute_qual_imputation(), compute_qual_imputation_dec(), compute_rr(), compute_scale()

Examples

compute_qtc(qt = 350, rr = 857, method = "Bazett")

compute_qtc(qt = 350, rr = 857, method = "Fridericia")

compute_qtc(qt = 350, rr = 857, method = "Sagie")

Description

Derive an imputed value

Usage

compute_qual_imputation(character_value, imputation_type = 1, factor = 0)

Arguments

Value

The imputed value

See Also

BDS-Findings Functions that returns a vector: compute_bmi(), compute_bsa(), compute_egfr(), compute_framingham(), compute_map(), compute_qtc(), compute_qual_imputation_dec(), compute_rr(), compute_scale()

Examples

compute_qual_imputation("<40")

Description

Function to compute factor for value imputation when character value contains < or >. The factor is calculated using the number of decimals. If there are no decimals, the factor is 1, otherwise the factor = 1/10^decimal place. For example, the factor for 100 = 1, the factor for 5.4 = 1/10^1, the factor for 5.44 = 1/10^2. This results in no additional false precision added to the value. This is an intermediate function.

Usage

compute_qual_imputation_dec(character_value_decimal)

Arguments

Details

Derive an imputed value

Value

Decimal precision value to add or subtract

See Also

BDS-Findings Functions that returns a vector: compute_bmi(), compute_bsa(), compute_egfr(), compute_framingham(), compute_map(), compute_qtc(), compute_qual_imputation(), compute_rr(), compute_scale()

Examples

compute_qual_imputation_dec("<40.1")

Description

Computes RR interval from heart rate.

Usage

compute_rr(hr)

Arguments

Details

Usually this computation function can not be used with ⁠%>%⁠.

Value

RR interval in ms:

$\frac{60000}{HR}$

See Also

derive_param_rr()

BDS-Findings Functions that returns a vector: compute_bmi(), compute_bsa(), compute_egfr(), compute_framingham(), compute_map(), compute_qtc(), compute_qual_imputation(), compute_qual_imputation_dec(), compute_scale()

Examples

compute_rr(hr = 70.14)

Description

Computes the average of a set of source values and transforms the result from the source range to the target range. For example, for calculating the average of a set of questionnaire response scores and re-coding the average response to obtain a subscale score.

Usage

compute_scale(
  source,
  source_range = NULL,
  target_range = NULL,
  flip_direction = FALSE,
  min_n = 1
)

Arguments

Details

Returns a numeric value. If source contains less than min_n values, the result is set to NA. If source_range and target_range aren't specified, the mean will be computed without any transformation being performed.

Value

The average of source transformed to the target range or NA if source doesn't contain min_n values.

See Also

BDS-Findings Functions that returns a vector: compute_bmi(), compute_bsa(), compute_egfr(), compute_framingham(), compute_map(), compute_qtc(), compute_qual_imputation(), compute_qual_imputation_dec(), compute_rr()

Examples

compute_scale(
  source = c(1, 4, 3, 5),
  source_range = c(1, 5),
  target_range = c(0, 100),
  flip_direction = TRUE,
  min_n = 3
)

Description

Derive the time imputation flag ('--TMF') comparing a date character vector ('--DTC') with a Datetime vector ('--DTM').

Usage

compute_tmf(dtc, dtm, ignore_seconds_flag = FALSE)

Arguments

Details

Usually this computation function can not be used with ⁠%>%⁠.

Value

The time imputation flag ('--TMF') (character value of 'H', 'M' , 'S' or NA)

See Also

Date/Time Computation Functions that returns a vector: compute_age_years(), compute_dtf(), compute_duration(), convert_date_to_dtm(), convert_dtc_to_dt(), convert_dtc_to_dtm(), impute_dtc_dt(), impute_dtc_dtm()

Examples

library(lubridate)

compute_tmf(dtc = "2019-07-18T15:25", dtm = ymd_hms("2019-07-18T15:25:00"))
compute_tmf(dtc = "2019-07-18T15", dtm = ymd_hms("2019-07-18T15:25:00"))
compute_tmf(dtc = "2019-07-18", dtm = ymd("2019-07-18"))
compute_tmf(dtc = "2022-05--T00:00", dtm = ymd_hms("2022-05-15T23:59:59"))
compute_tmf(dtc = "2022-05--T23:00", dtm = ymd_hms("2022-05-15T23:59:59"))
compute_tmf(dtc = "2022-05--T23:59:00", dtm = ymd_hms("2022-05-15T23:59:59"))

Description

The purpose of the function is to consolidate multiple meta datasets into a single one. For example, from global and project specific parameter mappings a single lookup table can be created.

Usage

consolidate_metadata(
  datasets,
  key_vars,
  source_var = SOURCE,
  check_vars = "warning",
  check_type = "error"
)

Arguments

Details

All observations of the input datasets are put together into a single dataset. If a by group (defined by key_vars) exists in more than one of the input datasets, the observation from the last dataset is selected.

Value

A dataset which contains one row for each by group occurring in any of the input datasets.

See Also

Creating auxiliary datasets: create_period_dataset(), create_query_data(), create_single_dose_dataset()

Examples

library(tibble)
glob_ranges <- tribble(
  ~PARAMCD, ~ANRLO, ~ANRHI,
  "PULSE",      60,    100,
  "SYSBP",      90,    130,
  "DIABP",      60,     80
)
proj_ranges <- tribble(
  ~PARAMCD, ~ANRLO, ~ANRHI,
  "SYSBP",     100,    140,
  "DIABP",      70,     90
)
stud_ranges <- tribble(
  ~PARAMCD, ~ANRLO, ~ANRHI,
  "BMI",        18,     25
)

consolidate_metadata(
  datasets = list(
    global = glob_ranges,
    project = proj_ranges,
    study = stud_ranges
  ),
  key_vars = exprs(PARAMCD)
)

Description

Turn SAS blank strings into proper R NAs.

Usage

convert_blanks_to_na(x)

## Default S3 method:
convert_blanks_to_na(x)

## S3 method for class 'character'
convert_blanks_to_na(x)

## S3 method for class 'list'
convert_blanks_to_na(x)

## S3 method for class 'data.frame'
convert_blanks_to_na(x)

Arguments

Details

The default methods simply returns its input unchanged. The character method turns every instance of "" into NA_character_ while preserving all attributes. When given a data frame as input the function keeps all non-character columns as is and applies the just described logic to character columns. Once again all attributes such as labels are preserved.

Value

An object of the same class as the input

See Also

Utilities for Formatting Observations: convert_na_to_blanks(), yn_to_numeric()

Examples

library(tibble)

convert_blanks_to_na(c("a", "b", "", "d", ""))

df <- tribble(
  ~USUBJID,   ~RFICDTC,
  "1001", "2000-01-01",
  "1002", "2001-01-01",
  "1003",           ""
)
print(df)
convert_blanks_to_na(df)

Description

Convert a date (datetime, date, or date character) into a Date vector (usually '--DTM').

Note: This is a wrapper function for the function convert_dtc_to_dtm().

Usage

convert_date_to_dtm(
  dt,
  highest_imputation = "h",
  date_imputation = "first",
  time_imputation = "first",
  min_dates = NULL,
  max_dates = NULL,
  preserve = FALSE
)

Arguments

impute_dtc_dtm(
  "2020-11",
  min_dates = list(
   ymd_hms("2020-12-06T12:12:12"),
   ymd_hms("2020-11-11T11:11:11")
  ),
  highest_imputation = "M"
)

Details

Usually this computation function can not be used with ⁠%>%⁠.

Value

A datetime object

See Also

Date/Time Computation Functions that returns a vector: compute_age_years(), compute_dtf(), compute_duration(), compute_tmf(), convert_dtc_to_dt(), convert_dtc_to_dtm(), impute_dtc_dt(), impute_dtc_dtm()

Examples

convert_date_to_dtm("2019-07-18T15:25:00")
convert_date_to_dtm(Sys.time())
convert_date_to_dtm(as.Date("2019-07-18"), time_imputation = "23:59:59")
convert_date_to_dtm("2019-07-18", time_imputation = "23:59:59")
convert_date_to_dtm("2019-07-18")

Description

Convert a date character vector (usually '–DTC') into a Date vector (usually '–DT').

Usage

convert_dtc_to_dt(
  dtc,
  highest_imputation = "n",
  date_imputation = "first",
  min_dates = NULL,
  max_dates = NULL,
  preserve = FALSE
)

Arguments

impute_dtc_dtm(
  "2020-11",
  min_dates = list(
   ymd_hms("2020-12-06T12:12:12"),
   ymd_hms("2020-11-11T11:11:11")
  ),
  highest_imputation = "M"
)

Details

Usually this computation function can not be used with ⁠%>%⁠.

Value

a date object

See Also

Date/Time Computation Functions that returns a vector: compute_age_years(), compute_dtf(), compute_duration(), compute_tmf(), convert_date_to_dtm(), convert_dtc_to_dtm(), impute_dtc_dt(), impute_dtc_dtm()

Examples

convert_dtc_to_dt("2019-07-18")
convert_dtc_to_dt("2019-07")

Description

Convert a date character vector (usually '--DTC') into a Date vector (usually '--DTM').

Usage

convert_dtc_to_dtm(
  dtc,
  highest_imputation = "h",
  date_imputation = "first",
  time_imputation = "first",
  min_dates = NULL,
  max_dates = NULL,
  preserve = FALSE
)

Arguments

impute_dtc_dtm(
  "2020-11",
  min_dates = list(
   ymd_hms("2020-12-06T12:12:12"),
   ymd_hms("2020-11-11T11:11:11")
  ),
  highest_imputation = "M"
)

Details

Usually this computation function can not be used with ⁠%>%⁠.

Value

A datetime object

See Also

Date/Time Computation Functions that returns a vector: compute_age_years(), compute_dtf(), compute_duration(), compute_tmf(), convert_date_to_dtm(), convert_dtc_to_dt(), impute_dtc_dt(), impute_dtc_dtm()

Examples

convert_dtc_to_dtm("2019-07-18T15:25:00")
convert_dtc_to_dtm("2019-07-18T00:00:00") # note Time = 00:00:00 is not printed
convert_dtc_to_dtm("2019-07-18")

Description

Turn NAs to blank strings .

Usage

convert_na_to_blanks(x)

## Default S3 method:
convert_na_to_blanks(x)

## S3 method for class 'character'
convert_na_to_blanks(x)

## S3 method for class 'list'
convert_na_to_blanks(x)

## S3 method for class 'data.frame'
convert_na_to_blanks(x)

Arguments

Details

The default methods simply returns its input unchanged. The character method turns every instance of NA_character_ or NA into "" while preserving all attributes. When given a data frame as input the function keeps all non-character columns as is and applies the just described logic to character all attributes such as labels are preserved.

Value

An object of the same class as the input

See Also

Utilities for Formatting Observations: convert_blanks_to_na(), yn_to_numeric()

Examples

library(tibble)

convert_na_to_blanks(c("a", "b", NA, "d", NA))

df <- tribble(
  ~USUBJID,   ~RFICDTC,
  "1001", "2000-01-01",
  "1002", "2001-01-01",
  "1003",           NA
)
print(df)
convert_na_to_blanks(df)

Description

Count number of observations where a variable equals a value.

Usage

count_vals(var, val)

Arguments

See Also

Utilities for Filtering Observations: filter_exist(), filter_extreme(), filter_joined(), filter_not_exist(), filter_relative(), max_cond(), min_cond()

Examples

library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(admiral)
data <- tribble(
  ~USUBJID, ~AVISITN, ~AVALC,
  "1",      1,        "PR",
  "1",      2,        "CR",
  "1",      3,        "NE",
  "1",      4,        "CR",
  "1",      5,        "NE",
  "2",      1,        "CR",
  "2",      2,        "PR",
  "2",      3,        "CR",
  "3",      1,        "CR",
  "4",      1,        "CR",
  "4",      2,        "NE",
  "4",      3,        "NE",
  "4",      4,        "CR",
  "4",      5,        "PR"
)

# add variable providing the number of NEs for each subject
group_by(data, USUBJID) %>%
  mutate(nr_nes = count_vals(var = AVALC, val = "NE"))

Description

These pre-defined country codes are sourced from ISO 3166 Standards. See also Wikipedia.

Usage

country_code_lookup

Format

An object of class tbl_df (inherits from tbl, data.frame) with 249 rows and 3 columns.

Details

country_code is the 3-letter ISO 3166-1 county code commonly found in the ADSL COUNTRY variable. country_name is the country long name corresponding to to the 3-letter code. country_number is the numeric code corresponding to an alphabetic sorting of the 3-letter codes.

To see the entire table in the console, run print(country_code_lookup).

See Also

dose_freq_lookup

Other metadata: atoxgr_criteria_ctcv4, atoxgr_criteria_ctcv5, atoxgr_criteria_daids, dose_freq_lookup

Examples

library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(lubridate)

# Create reference dataset for periods
adsl <- tribble(
  ~USUBJID, ~SEX, ~COUNTRY,
  "ST01-01", "F", "AUT",
  "ST01-02", "M", "MWI",
  "ST01-03", "F", "GBR",
  "ST01-04", "M", "CHE",
  "ST01-05", "M", "NOR",
  "ST01-06", "F", "JPN",
  "ST01-07", "F", "USA"
)

covar <- adsl %>%
  derive_vars_merged(
    dataset_add = country_code_lookup,
    new_vars = exprs(COUNTRYN = country_number, COUNTRYL = country_name),
    by_vars = exprs(COUNTRY = country_code)
  )
covar

Description

The function creates a reference dataset for subperiods, periods, or phases from the ADSL dataset. The reference dataset can be used to derive subperiod, period, or phase variables like ASPER, ASPRSDT, ASPREDT, APERIOD, APERSDT, APEREDT, TRTA, APHASEN, PHSDTM, PHEDTM, ... in OCCDS and BDS datasets.

Usage

create_period_dataset(
  dataset,
  new_vars,
  subject_keys = get_admiral_option("subject_keys")
)

Arguments

Details

For each subject and each subperiod/period/phase where at least one of the source variable is not NA an observation is added to the output dataset.

Depending on the type of the source variable (subperiod, period, or phase) the variable ASPER, APERIOD, or APHASEN is added and set to the number of the subperiod, period, or phase.

The variables specified for new_vars (left hand side) are added to the output dataset and set to the value of the source variable (right hand side).

Value

A period reference dataset (see "Details" section)

See Also

derive_vars_period()

Creating auxiliary datasets: consolidate_metadata(), create_query_data(), create_single_dose_dataset()

Examples

library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(lubridate)

# Create reference dataset for periods
adsl <- tribble(
  ~USUBJID, ~AP01SDT,     ~AP01EDT,     ~AP02SDT,     ~AP02EDT,     ~TRT01A, ~TRT02A,
  "1",      "2021-01-04", "2021-02-06", "2021-02-07", "2021-03-07", "A",     "B",
  "2",      "2021-02-02", "2021-03-02", "2021-03-03", "2021-04-01", "B",     "A",
) %>%
  mutate(
    across(matches("AP\\d\\d[ES]DT"), ymd)
  ) %>%
  mutate(
    STUDYID = "xyz"
  )

create_period_dataset(
  adsl,
  new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT, TRTA = TRTxxA)
)

# Create reference dataset for phases
adsl <- tribble(
  ~USUBJID, ~PH1SDT,      ~PH1EDT,      ~PH2SDT,      ~PH2EDT,      ~APHASE1,    ~APHASE2,
  "1",      "2021-01-04", "2021-02-06", "2021-02-07", "2021-03-07", "TREATMENT", "FUP",
  "2",      "2021-02-02", "2021-03-02", NA,           NA,           "TREATMENT", NA
) %>%
  mutate(
    across(matches("PH\\d[ES]DT"), ymd)
  ) %>%
  mutate(
    STUDYID = "xyz"
  )

create_period_dataset(
  adsl,
  new_vars = exprs(PHSDT = PHwSDT, PHEDT = PHwEDT, APHASE = APHASEw)
)

# Create reference datasets for subperiods
adsl <- tribble(
  ~USUBJID, ~P01S1SDT,    ~P01S1EDT,    ~P01S2SDT,    ~P01S2EDT,    ~P02S1SDT,    ~P02S1EDT,
  "1",      "2021-01-04", "2021-01-19", "2021-01-20", "2021-02-06", "2021-02-07", "2021-03-07",
  "2",      "2021-02-02", "2021-03-02", NA,           NA,           "2021-03-03", "2021-04-01"
) %>%
  mutate(
    across(matches("P\\d\\dS\\d[ES]DT"), ymd)
  ) %>%
  mutate(
    STUDYID = "xyz"
  )

create_period_dataset(
  adsl,
  new_vars = exprs(ASPRSDT = PxxSwSDT, ASPREDT = PxxSwEDT)
)

Description

Creates a queries dataset as input dataset to the dataset_queries argument in the derive_vars_query() function as defined in the Queries Dataset Documentation.

Usage

create_query_data(queries, version = NULL, get_terms_fun = NULL)

Arguments

Details

For each query() object listed in the queries argument, the terms belonging to the query (SRCVAR, TERMCHAR, TERMNUM) are determined with respect to the definition field of the query: if the definition field of the query() object is

• a basket_select() object, the terms are read from the basket database by calling the function specified for the get_terms_fun parameter.

• a data frame, the terms stored in the data frame are used.

• a list of data frames and basket_select() objects, all terms from the data frames and all terms read from the basket database referenced by the basket_select() objects are collated.

The following variables (as described in Queries Dataset Documentation) are created:

• PREFIX: Prefix of the variables to be created by derive_vars_query() as specified by the prefix element.

• GRPNAME: Name of the query as specified by the name element.

• GRPID: Id of the query as specified by the id element. If the id element is not specified for a query, the variable is set to NA. If the id element is not specified for any query, the variable is not created.

• SCOPE: scope of the query as specified by the scope element of the basket_select() object. For queries not defined by a basket_select() object, the variable is set to NA. If none of the queries is defined by a basket_select() object, the variable is not created.

• SCOPEN: numeric scope of the query. It is set to 1 if the scope is broad. Otherwise it is set to 2. If the add_scope_num element equals FALSE, the variable is set to NA. If the add_scope_num element equals FALSE for all baskets or none of the queries is an basket , the variable is not created.

• SRCVAR: Name of the variable used to identify the terms.

• TERMCHAR: Value of the term variable if it is a character variable.

• TERMNUM: Value of the term variable if it is a numeric variable.

• VERSION: Set to the value of the version argument. If it is not specified, the variable is not created.

Value

A dataset to be used as input dataset to the dataset_queries argument in derive_vars_query()

See Also

derive_vars_query(), query(), basket_select(), Queries Dataset Documentation

Creating auxiliary datasets: consolidate_metadata(), create_period_dataset(), create_single_dose_dataset()

Examples

library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(pharmaversesdtm)
library(admiral)

# creating a query dataset for a customized query
cqterms <- tribble(
  ~TERMCHAR, ~TERMNUM,
  "APPLICATION SITE ERYTHEMA", 10003041L,
  "APPLICATION SITE PRURITUS", 10003053L
) %>%
  mutate(SRCVAR = "AEDECOD")

cq <- query(
  prefix = "CQ01",
  name = "Application Site Issues",
  definition = cqterms
)

create_query_data(queries = list(cq))

# create a query dataset for SMQs
pregsmq <- query(
  prefix = "SMQ02",
  id = auto,
  definition = basket_select(
    name = "Pregnancy and neonatal topics (SMQ)",
    scope = "NARROW",
    type = "smq"
  )
)

bilismq <- query(
  prefix = "SMQ04",
  definition = basket_select(
    id = 20000121L,
    scope = "BROAD",
    type = "smq"
  )
)

# The get_terms function from pharmaversesdtm is used for this example.
# In a real application a company-specific function must be used.
create_query_data(
  queries = list(pregsmq, bilismq),
  get_terms_fun = pharmaversesdtm:::get_terms,
  version = "20.1"
)

# create a query dataset for SDGs
sdg <- query(
  prefix = "SDG01",
  id = auto,
  definition = basket_select(
    name = "5-aminosalicylates for ulcerative colitis",
    scope = NA_character_,
    type = "sdg"
  )
)

# The get_terms function from pharmaversesdtm is used for this example.
# In a real application a company-specific function must be used.
create_query_data(
  queries = list(sdg),
  get_terms_fun = pharmaversesdtm:::get_terms,
  version = "2019-09"
)

# creating a query dataset for a customized query including SMQs
# The get_terms function from pharmaversesdtm is used for this example.
# In a real application a company-specific function must be used.
create_query_data(
  queries = list(
    query(
      prefix = "CQ03",
      name = "Special issues of interest",
      definition = list(
        basket_select(
          name = "Pregnancy and neonatal topics (SMQ)",
          scope = "NARROW",
          type = "smq"
        ),
        cqterms
      )
    )
  ),
  get_terms_fun = pharmaversesdtm:::get_terms,
  version = "20.1"
)

Description

Derives dataset of single dose from aggregate dose information. This may be necessary when e.g. calculating last dose before an adverse event in ADAE or deriving a total dose parameter in ADEX when EXDOSFRQ != ONCE.

Usage

create_single_dose_dataset(
  dataset,
  dose_freq = EXDOSFRQ,
  start_date = ASTDT,
  start_datetime = NULL,
  end_date = AENDT,
  end_datetime = NULL,
  lookup_table = dose_freq_lookup,
  lookup_column = CDISC_VALUE,
  nominal_time = NULL,
  keep_source_vars = expr_c(get_admiral_option("subject_keys"), dose_freq, start_date,
    start_datetime, end_date, end_datetime)
)

Arguments

Details

Each aggregate dose row is split into multiple rows which each represent a single dose.The number of completed dose periods between start_date or start_datetime and end_date or end_datetime is calculated with compute_duration and multiplied by DOSE_COUNT. For DOSE_WINDOW values of "WEEK", "MONTH", and "YEAR", CONVERSION_FACTOR is used to convert into days the time object to be added to start_date.

Observations with dose frequency "ONCE" are copied to the output dataset unchanged.

Value

The input dataset with a single dose per row.

See Also

Creating auxiliary datasets: consolidate_metadata(), create_period_dataset(), create_query_data()

Examples

# Example with default lookup

library(lubridate)
library(stringr)
library(tibble)
library(dplyr)

data <- tribble(
  ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,
  "STUDY01", "P01", "Q2D", ymd("2021-01-01"), ymd_hms("2021-01-01 10:30:00"),
  ymd("2021-01-07"), ymd_hms("2021-01-07 11:30:00"),
  "STUDY01", "P01", "Q3D", ymd("2021-01-08"), ymd_hms("2021-01-08 12:00:00"),
  ymd("2021-01-14"), ymd_hms("2021-01-14 14:00:00"),
  "STUDY01", "P01", "EVERY 2 WEEKS", ymd("2021-01-15"), ymd_hms("2021-01-15 09:57:00"),
  ymd("2021-01-29"), ymd_hms("2021-01-29 10:57:00")
)

create_single_dose_dataset(data)

# Example with custom lookup

custom_lookup <- tribble(
  ~Value,   ~DOSE_COUNT, ~DOSE_WINDOW, ~CONVERSION_FACTOR,
  "Q30MIN", (1 / 30),    "MINUTE",                      1,
  "Q90MIN", (1 / 90),    "MINUTE",                      1
)

data <- tribble(
  ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,
  "STUDY01", "P01", "Q30MIN", ymd("2021-01-01"), ymd_hms("2021-01-01T06:00:00"),
  ymd("2021-01-01"), ymd_hms("2021-01-01T07:00:00"),
  "STUDY02", "P02", "Q90MIN", ymd("2021-01-01"), ymd_hms("2021-01-01T06:00:00"),
  ymd("2021-01-01"), ymd_hms("2021-01-01T09:00:00")
)

create_single_dose_dataset(data,
  lookup_table = custom_lookup,
  lookup_column = Value,
  start_datetime = ASTDTM,
  end_datetime = AENDTM
)
# Example with nominal time

data <- tribble(
  ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~NFRLT, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,
  "STUDY01", "P01", "BID", 0, ymd("2021-01-01"), ymd_hms("2021-01-01 08:00:00"),
  ymd("2021-01-07"), ymd_hms("2021-01-07 20:00:00"),
  "STUDY01", "P01", "BID", 168, ymd("2021-01-08"), ymd_hms("2021-01-08 08:00:00"),
  ymd("2021-01-14"), ymd_hms("2021-01-14 20:00:00"),
  "STUDY01", "P01", "BID", 336, ymd("2021-01-15"), ymd_hms("2021-01-15 08:00:00"),
  ymd("2021-01-29"), ymd_hms("2021-01-29 20:00:00")
)

create_single_dose_dataset(data,
  dose_freq = EXDOSFRQ,
  start_date = ASTDT,
  start_datetime = ASTDTM,
  end_date = AENDT,
  end_datetime = AENDTM,
  lookup_table = dose_freq_lookup,
  lookup_column = CDISC_VALUE,
  nominal_time = NFRLT,
  keep_source_vars = exprs(
    USUBJID, EXDOSFRQ, ASTDT, ASTDTM, AENDT, AENDTM, NFRLT
  )
)

# Example - derive a single dose dataset with imputations

# For either single drug administration records, or multiple drug administration
# records covering a range of dates, fill-in of missing treatment end datetime
# `EXENDTC` by substitution with an acceptable alternate, for example date of
# death, date of datacut may be required. This example shows the
# maximum possible number of single dose records to be derived. The example
# requires the date of datacut `DCUTDT` to be specified correctly, or
# if not appropriate to use `DCUTDT` as missing treatment end data and missing
# treatment end datetime could set equal to treatment start date and treatment
# start datetime. ADSL variables `DTHDT` and `DCUTDT` are preferred for
# imputation use.
#
# All available trial treatments are included, allowing multiple different
# last dose variables to be created in for example `use_ad_template("ADAE")`
# if required.

adsl <- tribble(
  ~STUDYID, ~USUBJID, ~DTHDT,
  "01", "1211", ymd("2013-01-14"),
  "01", "1083", ymd("2013-08-02"),
  "01", "1445", ymd("2014-11-01"),
  "01", "1015", NA,
  "01", "1023", NA
)

ex <- tribble(
  ~STUDYID, ~USUBJID, ~EXSEQ, ~EXTRT, ~EXDOSE, ~EXDOSU, ~EXDOSFRQ, ~EXSTDTC, ~EXENDTC,
  "01", "1015", 1, "PLAC", 0, "mg", "QD", "2014-01-02", "2014-01-16",
  "01", "1015", 2, "PLAC", 0, "mg", "QD", "2014-06-17", "2014-06-18",
  "01", "1015", 3, "PLAC", 0, "mg", "QD", "2014-06-19", NA_character_,
  "01", "1023", 1, "PLAC", 0, "mg", "QD", "2012-08-05", "2012-08-27",
  "01", "1023", 2, "PLAC", 0, "mg", "QD", "2012-08-28", "2012-09-01",
  "01", "1211", 1, "XANO", 54, "mg", "QD", "2012-11-15", "2012-11-28",
  "01", "1211", 2, "XANO", 54, "mg", "QD", "2012-11-29", NA_character_,
  "01", "1445", 1, "PLAC", 0, "mg", "QD", "2014-05-11", "2014-05-25",
  "01", "1445", 2, "PLAC", 0, "mg", "QD", "2014-05-26", "2014-11-01",
  "01", "1083", 1, "PLAC", 0, "mg", "QD", "2013-07-22", "2013-08-01"
)

adsl_death <- adsl %>%
  mutate(
    DTHDTM = convert_date_to_dtm(DTHDT),
    # Remove `DCUT` setup line below if ADSL `DCUTDT` is populated.
    DCUTDT = convert_dtc_to_dt("2015-03-06"), # Example only, enter date.
    DCUTDTM = convert_date_to_dtm(DCUTDT)
  )

# Select valid dose records, non-missing `EXSTDTC` and `EXDOSE`.
ex_mod <- ex %>%
  filter(!is.na(EXSTDTC) & !is.na(EXDOSE)) %>%
  derive_vars_merged(adsl_death, by_vars = get_admiral_option("subject_keys")) %>%
  # Example, set up missing `EXDOSFRQ` as QD daily dosing regime.
  # Replace with study dosing regime per trial treatment.
  mutate(EXDOSFRQ = if_else(is.na(EXDOSFRQ), "QD", EXDOSFRQ)) %>%
  # Create EXxxDTM variables and replace missing `EXENDTM`.
  derive_vars_dtm(
    dtc = EXSTDTC,
    new_vars_prefix = "EXST",
    date_imputation = "first",
    time_imputation = "first",
    flag_imputation = "none",
  ) %>%
  derive_vars_dtm_to_dt(exprs(EXSTDTM)) %>%
  derive_vars_dtm(
    dtc = EXENDTC,
    new_vars_prefix = "EXEN",
    # Maximum imputed treatment end date must not be not greater than
    # date of death or after the datacut date.
    max_dates = exprs(DTHDTM, DCUTDTM),
    date_imputation = "last",
    time_imputation = "last",
    flag_imputation = "none",
    highest_imputation = "Y",
  ) %>%
  derive_vars_dtm_to_dt(exprs(EXENDTM)) %>%
  # Select only unique values.
  # Removes duplicated records before final step.
  distinct(
    STUDYID, USUBJID, EXTRT, EXDOSE, EXDOSFRQ, DCUTDT, DTHDT, EXSTDT,
    EXSTDTM, EXENDT, EXENDTM, EXSTDTC, EXENDTC
  )

create_single_dose_dataset(
  ex_mod,
  start_date = EXSTDT,
  start_datetime = EXSTDTM,
  end_date = EXENDT,
  end_datetime = EXENDTM,
  keep_source_vars = exprs(
    STUDYID, USUBJID, EXTRT, EXDOSE, EXDOSFRQ,
    DCUTDT, EXSTDT, EXSTDTM, EXENDT, EXENDTM, EXSTDTC, EXENDTC
  )
)

Description

The date_source() function has been superseded in favor of derive_vars_extreme_event().

Create a date_source object as input for derive_var_extreme_dt() and derive_var_extreme_dtm().

Usage

date_source(dataset_name, filter = NULL, date, set_values_to = NULL)

Arguments

Value

An object of class date_source.

See Also

derive_var_extreme_dtm(), derive_var_extreme_dt()

Other superseded: derive_param_extreme_record(), derive_var_dthcaus(), derive_var_extreme_dt(), derive_var_extreme_dtm(), dthcaus_source(), get_summary_records()

Examples

# treatment end date from ADSL
trt_end_date <- date_source(
  dataset_name = "adsl",
  date = TRTEDT
)

# lab date from LB where assessment was taken, i.e. not "NOT DONE"
lb_date <- date_source(
  dataset_name = "lb",
  filter = LBSTAT != "NOT DONE" | is.na(LBSTAT),
  date = convert_dtc_to_dt(LBDTC)
)

# death date from ADSL including traceability variables
death_date <- date_source(
  dataset_name = "adsl",
  date = DTHDT,
  set_values_to = exprs(
    LALVDOM = "ADSL",
    LALVVAR = "DTHDT"
  )
)

Description

These pre-defined tte_source objects can be used as input to derive_param_tte().

Usage

death_event

lastalive_censor

ae_event

ae_ser_event

ae_gr1_event

ae_gr2_event

ae_gr3_event

ae_gr4_event

ae_gr5_event

ae_gr35_event

ae_sev_event

ae_wd_event

Details

To see the definition of the various objects simply print the object in the R console, e.g. print(death_event). For details of how to use these objects please refer to derive_param_tte().

See Also

derive_param_tte(), tte_source(), event_source(), censor_source()

Source Objects: basket_select(), censor_source(), event(), event_joined(), event_source(), flag_event(), query(), records_source(), tte_source()

Examples

# This shows the definition of all pre-defined `tte_source` objects that ship
# with {admiral}
for (obj in list_tte_source_objects()$object) {
  cat(obj, "\n")
  print(get(obj))
  cat("\n")
}

Description

Get Default Parameter Code for Corrected QT

Usage

default_qtc_paramcd(method)

Arguments

Value

"QTCBR" if method is "Bazett", "QTCFR" if it's "Fridericia" or "QTLCR" if it's "Sagie". An error otherwise.

See Also

derive_param_qtc()

BDS-Findings Functions for adding Parameters/Records: derive_expected_records(), derive_extreme_event(), derive_extreme_records(), derive_locf_records(), derive_param_bmi(), derive_param_bsa(), derive_param_computed(), derive_param_doseint(), derive_param_exist_flag(), derive_param_exposure(), derive_param_framingham(), derive_param_map(), derive_param_qtc(), derive_param_rr(), derive_param_wbc_abs(), derive_summary_records()

Examples

default_qtc_paramcd("Sagie")

Description

Create a derivation_slice object as input for slice_derivation().

Usage

derivation_slice(filter, args = NULL)

Arguments

Value

An object of class derivation_slice

See Also

slice_derivation(), params()

Higher Order Functions: call_derivation(), restrict_derivation(), slice_derivation()

Description

Baseline Type BASETYPE is needed when there is more than one definition of baseline for a given Analysis Parameter PARAM in the same dataset. For a given parameter, if Baseline Value BASE is populated, and there is more than one definition of baseline, then BASETYPE must be non-null on all records of any type for that parameter. Each value of BASETYPE refers to a definition of baseline that characterizes the value of BASE on that row. Please see section 4.2.1.6 of the ADaM Implementation Guide, version 1.3 for further background.

Usage

derive_basetype_records(dataset, basetypes)

Arguments

Details

Adds the BASETYPE variable to a dataset and duplicates records based upon the provided conditions.

For each element of basetypes the input dataset is subset based upon the provided expression and the BASETYPE variable is set to the name of the expression. Then, all subsets are stacked. Records which do not match any condition are kept and BASETYPE is set to NA.

Value

The input dataset with variable BASETYPE added

See Also

BDS-Findings Functions that returns variable appended to dataset: derive_var_analysis_ratio(), derive_var_anrind(), derive_var_atoxgr(), derive_var_atoxgr_dir(), derive_var_base(), derive_var_chg(), derive_var_ontrtfl(), derive_var_pchg(), derive_var_shift(), derive_vars_crit_flag()

Examples

library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(rlang)

bds <- tribble(
  ~USUBJID, ~EPOCH,         ~PARAMCD,  ~ASEQ, ~AVAL,
  "P01",    "RUN-IN",       "PARAM01",     1,  10.0,
  "P01",    "RUN-IN",       "PARAM01",     2,   9.8,
  "P01",    "DOUBLE-BLIND", "PARAM01",     3,   9.2,
  "P01",    "DOUBLE-BLIND", "PARAM01",     4,  10.1,
  "P01",    "OPEN-LABEL",   "PARAM01",     5,  10.4,
  "P01",    "OPEN-LABEL",   "PARAM01",     6,   9.9,
  "P02",    "RUN-IN",       "PARAM01",     1,  12.1,
  "P02",    "DOUBLE-BLIND", "PARAM01",     2,  10.2,
  "P02",    "DOUBLE-BLIND", "PARAM01",     3,  10.8,
  "P02",    "OPEN-LABEL",   "PARAM01",     4,  11.4,
  "P02",    "OPEN-LABEL",   "PARAM01",     5,  10.8
)

bds_with_basetype <- derive_basetype_records(
  dataset = bds,
  basetypes = exprs(
    "RUN-IN" = EPOCH %in% c("RUN-IN", "STABILIZATION", "DOUBLE-BLIND", "OPEN-LABEL"),
    "DOUBLE-BLIND" = EPOCH %in% c("DOUBLE-BLIND", "OPEN-LABEL"),
    "OPEN-LABEL" = EPOCH == "OPEN-LABEL"
  )
)


# Below print statement will print all 23 records in the data frame
# bds_with_basetype
print(bds_with_basetype, n = Inf)

count(bds_with_basetype, BASETYPE, name = "Number of Records")

# An example where all parameter records need to be included for 2 different
# baseline type derivations (such as LAST and WORST)
bds <- tribble(
  ~USUBJID, ~EPOCH,         ~PARAMCD,  ~ASEQ, ~AVAL,
  "P01",    "RUN-IN",       "PARAM01",     1,  10.0,
  "P01",    "RUN-IN",       "PARAM01",     2,   9.8,
  "P01",    "DOUBLE-BLIND", "PARAM01",     3,   9.2,
  "P01",    "DOUBLE-BLIND", "PARAM01",     4,  10.1
)

bds_with_basetype <- derive_basetype_records(
  dataset = bds,
  basetypes = exprs(
    "LAST" = TRUE,
    "WORST" = TRUE
  )
)

print(bds_with_basetype, n = Inf)

count(bds_with_basetype, BASETYPE, name = "Number of Records")

Description

Add expected records as new observations for each 'by group' when the dataset contains missing observations.

Usage

derive_expected_records(
  dataset,
  dataset_ref,
  by_vars = NULL,
  set_values_to = NULL
)

Arguments

Details

For each group (the variables specified in the by_vars parameter), those records from dataset_ref that are missing in the input dataset are added to the output dataset.

Value

The input dataset with the missed expected observations added for each by_vars. Note, a variable will only be populated in the new parameter rows if it is specified in by_vars or set_values_to.

See Also

BDS-Findings Functions for adding Parameters/Records: default_qtc_paramcd(), derive_extreme_event(), derive_extreme_records(), derive_locf_records(), derive_param_bmi(), derive_param_bsa(), derive_param_computed(), derive_param_doseint(), derive_param_exist_flag(), derive_param_exposure(), derive_param_framingham(), derive_param_map(), derive_param_qtc(), derive_param_rr(), derive_param_wbc_abs(), derive_summary_records()

Examples

library(tibble)

adqs <- tribble(
  ~USUBJID, ~PARAMCD, ~AVISITN, ~AVISIT, ~AVAL,
  "1",      "a",             1, "WEEK 1",   10,
  "1",      "b",             1, "WEEK 1",   11,
  "2",      "a",             2, "WEEK 2",   12,
  "2",      "b",             2, "WEEK 2",   14
)

# Example 1. visit variables are parameter independent
parm_visit_ref <- tribble(
  ~AVISITN, ~AVISIT,
  1,        "WEEK 1",
  2,        "WEEK 2"
)

derive_expected_records(
  dataset = adqs,
  dataset_ref = parm_visit_ref,
  by_vars = exprs(USUBJID, PARAMCD),
  set_values_to = exprs(DTYPE = "DERIVED")
)

# Example 2. visit variables are parameter dependent
parm_visit_ref <- tribble(
  ~PARAMCD, ~AVISITN, ~AVISIT,
  "a",             1, "WEEK 1",
  "a",             2, "WEEK 2",
  "b",             1, "WEEK 1"
)

derive_expected_records(
  dataset = adqs,
  dataset_ref = parm_visit_ref,
  by_vars = exprs(USUBJID, PARAMCD),
  set_values_to = exprs(DTYPE = "DERIVED")
)

Description

Add the first available record from events for each by group as new records, all variables of the selected observation are kept. It can be used for selecting the extreme observation from a series of user-defined events. This distinguishes derive_extreme_event() from derive_extreme_records(), where extreme records are derived based on certain order of existing variables.

Usage

derive_extreme_event(
  dataset = NULL,
  by_vars,
  events,
  tmp_event_nr_var = NULL,
  order,
  mode,
  source_datasets = NULL,
  check_type = "warning",
  set_values_to = NULL,
  keep_source_vars = exprs(everything())
)

Arguments

  set_values_to = exprs(
    AVAL = sum(AVAL),
    DTYPE = "AVERAGE",
  )

Details

1. For each event select the observations to consider:

   1. If the event is of class event, the observations of the source dataset are restricted by condition and then the first or last (mode) observation per by group (by_vars) is selected.

      If the event is of class event_joined, filter_joined() is called to select the observations.

   2. The variables specified by the set_values_to field of the event are added to the selected observations.

   3. The variable specified for tmp_event_nr_var is added and set to the number of the event.

   4. Only the variables specified for the keep_source_vars field of the event, and the by variables (by_vars) and the variables created by set_values_to are kept. If keep_source_vars = NULL is used for an event in derive_extreme_event() the value of the keep_source_vars argument of derive_extreme_event() is used.

2. All selected observations are bound together.

3. For each group (with respect to the variables specified for the by_vars parameter) the first or last observation (with respect to the order specified for the order parameter and the mode specified for the mode parameter) is selected.

4. The variables specified by the set_values_to parameter are added to the selected observations.

5. The observations are added to input dataset.

Value

The input dataset with the best or worst observation of each by group added as new observations.

See Also

event(), event_joined(), derive_vars_extreme_event()

BDS-Findings Functions for adding Parameters/Records: default_qtc_paramcd(), derive_expected_records(), derive_extreme_records(), derive_locf_records(), derive_param_bmi(), derive_param_bsa(), derive_param_computed(), derive_param_doseint(), derive_param_exist_flag(), derive_param_exposure(), derive_param_framingham(), derive_param_map(), derive_param_qtc(), derive_param_rr(), derive_param_wbc_abs(), derive_summary_records()

Examples

library(tibble)
library(dplyr)
library(lubridate)

adqs <- tribble(
  ~USUBJID, ~PARAMCD,       ~AVALC,        ~ADY,
  "1",      "NO SLEEP",     "N",              1,
  "1",      "WAKE UP",      "N",              2,
  "1",      "FALL ASLEEP",  "N",              3,
  "2",      "NO SLEEP",     "N",              1,
  "2",      "WAKE UP",      "Y",              2,
  "2",      "WAKE UP",      "Y",              3,
  "2",      "FALL ASLEEP",  "N",              4,
  "3",      "NO SLEEP",     NA_character_,    1
)

# Add a new record for each USUBJID storing the the worst sleeping problem.
derive_extreme_event(
  adqs,
  by_vars = exprs(USUBJID),
  events = list(
    event(
      condition = PARAMCD == "NO SLEEP" & AVALC == "Y",
      set_values_to = exprs(AVALC = "No sleep", AVAL = 1)
    ),
    event(
      condition = PARAMCD == "WAKE UP" & AVALC == "Y",
      set_values_to = exprs(AVALC = "Waking up more than three times", AVAL = 2)
    ),
    event(
      condition = PARAMCD == "FALL ASLEEP" & AVALC == "Y",
      set_values_to = exprs(AVALC = "More than 30 mins to fall asleep", AVAL = 3)
    ),
    event(
      condition = all(AVALC == "N"),
      set_values_to = exprs(
        AVALC = "No sleeping problems", AVAL = 4
      )
    ),
    event(
      condition = TRUE,
      set_values_to = exprs(AVALC = "Missing", AVAL = 99)
    )
  ),
  tmp_event_nr_var = event_nr,
  order = exprs(event_nr, desc(ADY)),
  mode = "first",
  set_values_to = exprs(
    PARAMCD = "WSP",
    PARAM = "Worst Sleeping Problems"
  )
)

# Use different mode by event
adhy <- tribble(
  ~USUBJID, ~AVISITN, ~CRIT1FL,
  "1",             1, "Y",
  "1",             2, "Y",
  "2",             1, "Y",
  "2",             2, NA_character_,
  "2",             3, "Y",
  "2",             4, NA_character_
) %>%
  mutate(
    PARAMCD = "ALKPH",
    PARAM = "Alkaline Phosphatase (U/L)"
  )

derive_extreme_event(
  adhy,
  by_vars = exprs(USUBJID),
  events = list(
    event(
      condition = is.na(CRIT1FL),
      set_values_to = exprs(AVALC = "N")
    ),
    event(
      condition = CRIT1FL == "Y",
      mode = "last",
      set_values_to = exprs(AVALC = "Y")
    )
  ),
  tmp_event_nr_var = event_nr,
  order = exprs(event_nr, AVISITN),
  mode = "first",
  keep_source_vars = exprs(AVISITN),
  set_values_to = exprs(
    PARAMCD = "ALK2",
    PARAM = "ALKPH <= 2 times ULN"
  )
)

# Derive confirmed best overall response (using event_joined())
# CR - complete response, PR - partial response, SD - stable disease
# NE - not evaluable, PD - progressive disease
adsl <- tribble(
  ~USUBJID, ~TRTSDTC,
  "1",      "2020-01-01",
  "2",      "2019-12-12",
  "3",      "2019-11-11",
  "4",      "2019-12-30",
  "5",      "2020-01-01",
  "6",      "2020-02-02",
  "7",      "2020-02-02",
  "8",      "2020-02-01"
) %>%
  mutate(TRTSDT = ymd(TRTSDTC))

adrs <- tribble(
  ~USUBJID, ~ADTC,        ~AVALC,
  "1",      "2020-01-01", "PR",
  "1",      "2020-02-01", "CR",
  "1",      "2020-02-16", "NE",
  "1",      "2020-03-01", "CR",
  "1",      "2020-04-01", "SD",
  "2",      "2020-01-01", "SD",
  "2",      "2020-02-01", "PR",
  "2",      "2020-03-01", "SD",
  "2",      "2020-03-13", "CR",
  "4",      "2020-01-01", "PR",
  "4",      "2020-03-01", "NE",
  "4",      "2020-04-01", "NE",
  "4",      "2020-05-01", "PR",
  "5",      "2020-01-01", "PR",
  "5",      "2020-01-10", "PR",
  "5",      "2020-01-20", "PR",
  "6",      "2020-02-06", "PR",
  "6",      "2020-02-16", "CR",
  "6",      "2020-03-30", "PR",
  "7",      "2020-02-06", "PR",
  "7",      "2020-02-16", "CR",
  "7",      "2020-04-01", "NE",
  "8",      "2020-02-16", "PD"
) %>%
  mutate(
    ADT = ymd(ADTC),
    PARAMCD = "OVR",
    PARAM = "Overall Response by Investigator"
  ) %>%
  derive_vars_merged(
    dataset_add = adsl,
    by_vars = exprs(USUBJID),
    new_vars = exprs(TRTSDT)
  )

derive_extreme_event(
  adrs,
  by_vars = exprs(USUBJID),
  tmp_event_nr_var = event_nr,
  order = exprs(event_nr, ADT),
  mode = "first",
  source_datasets = list(adsl = adsl),
  events = list(
    event_joined(
      description = paste(
        "CR needs to be confirmed by a second CR at least 28 days later",
        "at most one NE is acceptable between the two assessments"
      ),
      join_vars = exprs(AVALC, ADT),
      join_type = "after",
      first_cond_upper = AVALC.join == "CR" &
        ADT.join >= ADT + 28,
      condition = AVALC == "CR" &
        all(AVALC.join %in% c("CR", "NE")) &
        count_vals(var = AVALC.join, val = "NE") <= 1,
      set_values_to = exprs(
        AVALC = "CR"
      )
    ),
    event_joined(
      description = paste(
        "PR needs to be confirmed by a second CR or PR at least 28 days later,",
        "at most one NE is acceptable between the two assessments"
      ),
      join_vars = exprs(AVALC, ADT),
      join_type = "after",
      first_cond_upper = AVALC.join %in% c("CR", "PR") &
        ADT.join >= ADT + 28,
      condition = AVALC == "PR" &
        all(AVALC.join %in% c("CR", "PR", "NE")) &
        count_vals(var = AVALC.join, val = "NE") <= 1,
      set_values_to = exprs(
        AVALC = "PR"
      )
    ),
    event(
      description = paste(
        "CR, PR, or SD are considered as SD if occurring at least 28",
        "after treatment start"
      ),
      condition = AVALC %in% c("CR", "PR", "SD") & ADT >= TRTSDT + 28,
      set_values_to = exprs(
        AVALC = "SD"
      )
    ),
    event(
      condition = AVALC == "PD",
      set_values_to = exprs(
        AVALC = "PD"
      )
    ),
    event(
      condition = AVALC %in% c("CR", "PR", "SD", "NE"),
      set_values_to = exprs(
        AVALC = "NE"
      )
    ),
    event(
      description = "set response to MISSING for patients without records in ADRS",
      dataset_name = "adsl",
      condition = TRUE,
      set_values_to = exprs(
        AVALC = "MISSING"
      ),
      keep_source_vars = exprs(TRTSDT)
    )
  ),
  set_values_to = exprs(
    PARAMCD = "CBOR",
    PARAM = "Best Confirmed Overall Response by Investigator"
  )
) %>%
  filter(PARAMCD == "CBOR")