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Merge an Existence Flag From Multiple Sources

Source: R/derive_var_merged_ef_msrc.R
derive_var_merged_ef_msrc.Rd

Adds a flag variable to the input dataset which indicates if there exists at least one observation in one of the source datasets fulfilling a certain condition. For example, if a dose adjustment flag should be added to ADEX but the dose adjustment information is collected in different datasets, e.g., EX, EC, and FA.

Usage

derive_var_merged_ef_msrc(
  dataset,
  by_vars,
  flag_events,
  source_datasets,
  new_var,
  true_value = "Y",
  false_value = NA_character_,
  missing_value = NA_character_
)

Arguments

dataset

Input dataset

The variables specified by the by_vars argument are expected to be in the dataset.

by_vars

Grouping variables

Permitted Values: list of variables created by exprs() e.g. exprs(USUBJID, VISIT)

flag_events

Flag events

A list of flag_event() objects is expected. For each event the condition (condition field) is evaluated in the source dataset referenced by the dataset_name field. If it evaluates to TRUE at least once, the new variable is set to true_value.

source_datasets

Source datasets

A named list of datasets is expected. The dataset_name field of flag_event() refers to the dataset provided in the list.

new_var

New variable

The specified variable is added to the input dataset.

true_value

True value

The new variable (new_var) is set to the specified value for all by groups for which at least one of the source object (sources) has the condition evaluate to TRUE.

The values of true_value, false_value, and missing_value must be of the same type.

false_value

False value

The new variable (new_var) is set to the specified value for all by groups which occur in at least one source (sources) but the condition never evaluates to TRUE.

The values of true_value, false_value, and missing_value must be of the same type.

missing_value

Values used for missing information

The new variable is set to the specified value for all by groups without observations in any of the sources (sources).

The values of true_value, false_value, and missing_value must be of the same type.

Value

The output dataset contains all observations and variables of the input dataset and additionally the variable specified for new_var.

Details

  1. For each flag_event() object specified for flag_events: The condition (condition) is evaluated in the dataset referenced by dataset_name. If the by_vars field is specified the dataset is grouped by the specified variables for evaluating the condition. If named elements are used in by_vars like by_vars = exprs(USUBJID, EXLNKID = ECLNKID), the variables are renamed after the evaluation. If the by_vars element is not specified, the observations are grouped by the variables specified for the by_vars argument.

  2. The new variable (new_var) is added to the input dataset and set to the true value (true_value) if for the by group at least one condition evaluates to TRUE in one of the sources. It is set to the false value (false_value) if for the by group at least one observation exists and for all observations the condition evaluates to FALSE or NA. Otherwise, it is set to the missing value (missing_value).

See also

flag_event()

General Derivation Functions for all ADaMs that returns variable appended to dataset: derive_var_extreme_flag(), derive_var_joined_exist_flag(), derive_var_merged_exist_flag(), derive_var_merged_summary(), derive_var_obs_number(), derive_var_relative_flag(), derive_vars_computed(), derive_vars_joined(), derive_vars_merged(), derive_vars_merged_lookup(), derive_vars_transposed()

Examples

library(dplyr)

# Derive a flag indicating anti-cancer treatment based on CM and PR
adsl <- tribble(
  ~USUBJID,
  "1",
  "2",
  "3",
  "4"
)

cm <- tribble(
  ~USUBJID, ~CMCAT,        ~CMSEQ,
  "1",      "ANTI-CANCER",      1,
  "1",      "GENERAL",          2,
  "2",      "GENERAL",          1,
  "3",      "ANTI-CANCER",      1
)

# Assuming all records in PR indicate cancer treatment
pr <- tibble::tribble(
  ~USUBJID, ~PRSEQ,
  "2",      1,
  "3",      1
)

derive_var_merged_ef_msrc(
  adsl,
  by_vars = exprs(USUBJID),
  flag_events = list(
    flag_event(
      dataset_name = "cm",
      condition = CMCAT == "ANTI-CANCER"
    ),
    flag_event(
      dataset_name = "pr"
    )
  ),
  source_datasets = list(cm = cm, pr = pr),
  new_var = CANCTRFL
)
#> # A tibble: 4 × 2
#>   USUBJID CANCTRFL
#>   <chr>   <chr>   
#> 1 1       Y       
#> 2 2       Y       
#> 3 3       Y       
#> 4 4       NA      

# Using different by variables depending on the source
# Add a dose adjustment flag to ADEX based on ADEX, EC, and FA
adex <- tribble(
  ~USUBJID, ~EXLNKID, ~EXADJ,
  "1",      "1",      "AE",
  "1",      "2",      NA_character_,
  "1",      "3",      NA_character_,
  "2",      "1",      NA_character_,
  "3",      "1",      NA_character_
)

ec <- tribble(
  ~USUBJID, ~ECLNKID, ~ECADJ,
  "1",      "3",      "AE",
  "3",      "1",      NA_character_
)

fa <- tribble(
  ~USUBJID, ~FALNKID, ~FATESTCD, ~FAOBJ,            ~FASTRESC,
  "3",      "1",      "OCCUR",   "DOSE ADJUSTMENT", "Y"
)

derive_var_merged_ef_msrc(
  adex,
  by_vars = exprs(USUBJID, EXLNKID),
  flag_events = list(
    flag_event(
      dataset_name = "ex",
      condition = !is.na(EXADJ)
    ),
    flag_event(
      dataset_name = "ec",
      condition = !is.na(ECADJ),
      by_vars = exprs(USUBJID, EXLNKID = ECLNKID)
    ),
    flag_event(
      dataset_name = "fa",
      condition = FATESTCD == "OCCUR" & FAOBJ == "DOSE ADJUSTMENT" & FASTRESC == "Y",
      by_vars = exprs(USUBJID, EXLNKID = FALNKID)
    )
  ),
  source_datasets = list(ex = adex, ec = ec, fa = fa),
  new_var = DOSADJFL
)
#> # A tibble: 5 × 4
#>   USUBJID EXLNKID EXADJ DOSADJFL
#>   <chr>   <chr>   <chr> <chr>   
#> 1 1       1       AE    Y       
#> 2 1       2       NA    NA      
#> 3 1       3       NA    Y       
#> 4 2       1       NA    NA      
#> 5 3       1       NA    Y