inst/templates/ad_adsl.R

In admiral: ADaM in R Asset Library

# Name: ADSL
#
# Label: Subject Level Analysis Dataset
#
# Input: dm, ex, ds
library(admiral)
library(pharmaversesdtm) # Contains example datasets from the CDISC pilot project
library(dplyr)
library(lubridate)
library(stringr)

# Load source datasets ----

# Use e.g. haven::read_sas to read in .sas7bdat, or other suitable functions
# as needed and assign to the variables below.
# For illustration purposes read in admiral test data

data("dm")
data("ds")
data("ex")
data("ae")
data("lb")

# When SAS datasets are imported into R using haven::read_sas(), missing
# character values from SAS appear as "" characters in R, instead of appearing
# as NA values. Further details can be obtained via the following link:
# https://pharmaverse.github.io/admiral/cran-release/articles/admiral.html#handling-of-missing-values # nolint

dm <- convert_blanks_to_na(dm)
ds <- convert_blanks_to_na(ds)
ex <- convert_blanks_to_na(ex)
ae <- convert_blanks_to_na(ae)
lb <- convert_blanks_to_na(lb)

# User defined functions ----

# Here are some examples of how you can create your own functions that
#  operates on vectors, which can be used in `mutate`.

# Grouping
format_racegr1 <- function(x) {
  case_when(
    x == "WHITE" ~ "White",
    x != "WHITE" ~ "Non-white",
    TRUE ~ "Missing"
  )
}

format_agegr1 <- function(x) {
  case_when(
    x < 18 ~ "<18",
    between(x, 18, 64) ~ "18-64",
    x > 64 ~ ">64",
    TRUE ~ "Missing"
  )
}

format_region1 <- function(x) {
  case_when(
    x %in% c("CAN", "USA") ~ "NA",
    !is.na(x) ~ "RoW",
    TRUE ~ "Missing"
  )
}

format_lddthgr1 <- function(x) {
  case_when(
    x <= 30 ~ "<= 30",
    x > 30 ~ "> 30",
    TRUE ~ NA_character_
  )
}

# EOSSTT mapping
format_eosstt <- function(x) {
  case_when(
    x %in% c("COMPLETED") ~ "COMPLETED",
    x %in% c("SCREEN FAILURE") ~ NA_character_,
    !is.na(x) ~ "DISCONTINUED",
    TRUE ~ "ONGOING"
  )
}

# Derivations ----
# impute start and end time of exposure to first and last respectively, do not impute date
ex_ext <- ex %>%
  derive_vars_dtm(
    dtc = EXSTDTC,
    new_vars_prefix = "EXST"
  ) %>%
  derive_vars_dtm(
    dtc = EXENDTC,
    new_vars_prefix = "EXEN",
    time_imputation = "last"
  )

adsl <- dm %>%
  ## derive treatment variables (TRT01P, TRT01A) ----
  # See also the "Visit and Period Variables" vignette
  # (https://pharmaverse.github.io/admiral/cran-release/articles/visits_periods.html#treatment_adsl)
  mutate(TRT01P = ARM, TRT01A = ACTARM) %>%
  ## derive treatment start date (TRTSDTM) ----
  derive_vars_merged(
    dataset_add = ex_ext,
    filter_add = (EXDOSE > 0 |
      (EXDOSE == 0 &
        str_detect(EXTRT, "PLACEBO"))) &
      !is.na(EXSTDTM),
    new_vars = exprs(TRTSDTM = EXSTDTM, TRTSTMF = EXSTTMF),
    order = exprs(EXSTDTM, EXSEQ),
    mode = "first",
    by_vars = exprs(STUDYID, USUBJID)
  ) %>%
  ## derive treatment end date (TRTEDTM) ----
  derive_vars_merged(
    dataset_add = ex_ext,
    filter_add = (EXDOSE > 0 |
      (EXDOSE == 0 &
        str_detect(EXTRT, "PLACEBO"))) & !is.na(EXENDTM),
    new_vars = exprs(TRTEDTM = EXENDTM, TRTETMF = EXENTMF),
    order = exprs(EXENDTM, EXSEQ),
    mode = "last",
    by_vars = exprs(STUDYID, USUBJID)
  ) %>%
  ## Derive treatment end/start date TRTSDT/TRTEDT ----
  derive_vars_dtm_to_dt(source_vars = exprs(TRTSDTM, TRTEDTM)) %>%
  ## derive treatment duration (TRTDURD) ----
  derive_var_trtdurd()

## Disposition dates, status ----
# convert character date to numeric date without imputation
ds_ext <- derive_vars_dt(
  ds,
  dtc = DSSTDTC,
  new_vars_prefix = "DSST"
)

# Screen fail date
adsl <- adsl %>%
  derive_vars_merged(
    dataset_add = ds_ext,
    by_vars = exprs(STUDYID, USUBJID),
    new_vars = exprs(SCRFDT = DSSTDT),
    filter_add = DSCAT == "DISPOSITION EVENT" & DSDECOD == "SCREEN FAILURE"
  ) %>%
  derive_vars_merged(
    dataset_add = ds_ext,
    by_vars = exprs(STUDYID, USUBJID),
    new_vars = exprs(EOSDT = DSSTDT),
    filter_add = DSCAT == "DISPOSITION EVENT" & DSDECOD != "SCREEN FAILURE"
  ) %>%
  # EOS status
  derive_vars_merged(
    dataset_add = ds_ext,
    by_vars = exprs(STUDYID, USUBJID),
    filter_add = DSCAT == "DISPOSITION EVENT",
    new_vars = exprs(EOSSTT = format_eosstt(DSDECOD))
  ) %>%
  # Last retrieval date
  derive_vars_merged(
    dataset_add = ds_ext,
    by_vars = exprs(STUDYID, USUBJID),
    new_vars = exprs(FRVDT = DSSTDT),
    filter_add = DSCAT == "OTHER EVENT" & DSDECOD == "FINAL RETRIEVAL VISIT"
  ) %>%
  # Derive Randomization Date
  derive_vars_merged(
    dataset_add = ds_ext,
    filter_add = DSDECOD == "RANDOMIZED",
    by_vars = exprs(STUDYID, USUBJID),
    new_vars = exprs(RANDDT = DSSTDT)
  ) %>%
  # Death date - impute partial date to first day/month
  derive_vars_dt(
    new_vars_prefix = "DTH",
    dtc = DTHDTC,
    highest_imputation = "M",
    date_imputation = "first"
  ) %>%
  # Relative Day of Death
  derive_vars_duration(
    new_var = DTHADY,
    start_date = TRTSDT,
    end_date = DTHDT
  ) %>%
  # Elapsed Days from Last Dose to Death
  derive_vars_duration(
    new_var = LDDTHELD,
    start_date = TRTEDT,
    end_date = DTHDT,
    add_one = FALSE
  )

## Last known alive date ----
## DTC variables are converted to numeric dates imputing missing day and month
## to the first
ae_start_date <- date_source(
  dataset_name = "ae",
  date = convert_dtc_to_dt(AESTDTC, highest_imputation = "M")
)
ae_end_date <- date_source(
  dataset_name = "ae",
  date = convert_dtc_to_dt(AEENDTC, highest_imputation = "M")
)
lb_date <- date_source(
  dataset_name = "lb",
  date = convert_dtc_to_dt(LBDTC, highest_imputation = "M")
)
trt_end_date <- date_source(
  dataset_name = "adsl",
  date = TRTEDT
)

adsl <- adsl %>%
  derive_var_extreme_dt(
    new_var = LSTALVDT,
    ae_start_date, ae_end_date, lb_date, trt_end_date,
    source_datasets = list(ae = ae, lb = lb, adsl = adsl),
    mode = "last"
  ) %>%
  derive_var_merged_exist_flag(
    dataset_add = ex,
    by_vars = exprs(STUDYID, USUBJID),
    new_var = SAFFL,
    condition = (EXDOSE > 0 | (EXDOSE == 0 & str_detect(EXTRT, "PLACEBO")))
  ) %>%
  ## Groupings and others variables ----
  mutate(
    RACEGR1 = format_racegr1(RACE),
    AGEGR1 = format_agegr1(AGE),
    REGION1 = format_region1(COUNTRY),
    LDDTHGR1 = format_lddthgr1(LDDTHELD),
    DTH30FL = if_else(LDDTHGR1 == "<= 30", "Y", NA_character_),
    DTHA30FL = if_else(LDDTHGR1 == "> 30", "Y", NA_character_),
    DTHB30FL = if_else(DTHDT <= TRTSDT + 30, "Y", NA_character_),
    DOMAIN = NULL
  )


# Save output ----

dir <- tempdir() # Change to whichever directory you want to save the dataset in
saveRDS(adsl, file = file.path(dir, "adsl.rds"), compress = "bzip2")