Disposition Dates (e.g. EOSDT)

The functions derive_vars_dt() and derive_vars_merged() can be used to derive a disposition date. First the character disposition date (DS.DSSTDTC) is converted to a numeric date (DSSTDT) calling derive_vars_dt(). Then the relevant disposition date is selected by adjusting the filter_add parameter.

To derive the End of Study date (EOSDT), a call could be:

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

adsl <- adsl %>%
  derive_vars_merged(
    dataset_add = ds_ext,
    by_vars = vars(STUDYID, USUBJID),
    new_vars = vars(EOSDT = DSSTDT),
    filter_add = DSCAT == "DISPOSITION EVENT" & DSDECOD != "SCREEN FAILURE"
  )

With DS:

We would get :

This call would return the input dataset with the column EOSDT added. This function allows the user to impute partial dates as well. If imputation is needed and the date is to be imputed to the first of the month, then set date_imputation = "FIRST".

Disposition Status (e.g. EOSSTT)

The function derive_var_disposition_status() can be used to derive a disposition status at a specific timepoint. The relevant disposition variable (DS.DSDECOD) is selected by adjusting the filter parameter and used to derive EOSSTT.

To derive the End of Study status (EOSSTT), a call could be:

adsl <- adsl %>%
  derive_var_disposition_status(
    dataset_ds = ds,
    new_var = EOSSTT,
    status_var = DSDECOD,
    filter_ds = DSCAT == "DISPOSITION EVENT"
  )

Link to DS.

This call would return the input dataset with the column EOSSTT added.

By default, the function will derive EOSSTT as

• "NOT STARTED" if DSDECOD is "SCREEN FAILURE" or "SCREENING NOT COMPLETED"
• "COMPLETED" if DSDECOD == "COMPLETED"
• "DISCONTINUED" if DSDECOD is not "COMPLETED" or NA
• "ONGOING" otherwise

If the default derivation must be changed, the user can create his/her own function and pass it to the format_new_var argument of the function (format_new_var = new_mapping) to map DSDECOD to a suitable EOSSTT value.

Example function format_eosstt():

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

The customized mapping function format_eosstt() can now be passed to the main function:

adsl <- adsl %>%
  derive_var_disposition_status(
    dataset_ds = ds,
    new_var = EOSSTT,
    status_var = DSDECOD,
    format_new_var = format_eosstt,
    filter_ds = DSCAT == "DISPOSITION EVENT"
  )

This call would return the input dataset with the column EOSSTT added.

Disposition Reason(s) (e.g. DCSREAS, DCSREASP)

The main reason for discontinuation is usually stored in DSDECOD while DSTERM provides additional details regarding subject’s discontinuation (e.g., description of "OTHER").

The function derive_vars_disposition_reason() can be used to derive a disposition reason (along with the details, if required) at a specific timepoint. The relevant disposition variable(s) (DS.DSDECOD, DS.DSTERM) are selected by adjusting the filter parameter and used to derive the main reason (and details).

To derive the End of Study reason(s) (DCSREAS and DCSREASP), the call would be:

adsl <- adsl %>%
  derive_vars_disposition_reason(
    dataset_ds = ds,
    new_var = DCSREAS,
    reason_var = DSDECOD,
    new_var_spe = DCSREASP,
    reason_var_spe = DSTERM,
    filter_ds = DSCAT == "DISPOSITION EVENT" & DSDECOD != "SCREEN FAILURE"
  )

Link to DS.

This call would return the input dataset with the column DCSREAS and DCSREASP added.

By default, the function will map

• DCSREAS as DSDECOD if DSDECOD is not "COMPLETED" or NA, NA otherwise
• DCSREASP as DSTERM if DSDECOD is equal to OTHER, NA otherwise

If the default derivation must be changed, the user can create his/her own function and pass it to the format_new_var argument of the function (format_new_var = new_mapping) to map DSDECOD and DSTERM to a suitable DCSREAS/DCSREASP value.

Example function format_dcsreas():

format_dcsreas <- function(dsdecod, dsterm = NULL) {
  if (is.null(dsterm)) {
    if_else(dsdecod %notin% c("COMPLETED", "SCREEN FAILURE") & !is.na(dsdecod), dsdecod, NA_character_)
  } else {
    if_else(dsdecod == "OTHER", dsterm, NA_character_)
  }
}

The customized mapping function format_dcsreas() can now be passed to the main function:

adsl <- adsl %>%
  derive_vars_disposition_reason(
    dataset_ds = ds,
    new_var = DCSREAS,
    reason_var = DSDECOD,
    new_var_spe = DCSREASP,
    reason_var_spe = DSTERM,
    format_new_vars = format_dcsreas,
    filter_ds = DSCAT == "DISPOSITION EVENT"
  )

Randomization Date (RANDDT)

The function derive_vars_merged() can be used to derive randomization date variable. To map Randomization Date (RANDDT), the call would be:

adsl <- adsl %>%
  derive_vars_merged(
    dataset_add = ds_ext,
    filter_add = DSDECOD == "RANDOMIZED",
    by_vars = vars(STUDYID, USUBJID),
    new_vars = vars(RANDDT = DSSTDT)
  )

This call would return the input dataset with the column RANDDT is added.

Link to DS.

Death Date (DTHDT)

The function derive_vars_dt() can be used to derive DTHDT. This function allows the user to impute the date as well.

Example calls:

adsl <- adsl %>%
  derive_vars_dt(
    new_vars_prefix = "DTH",
    dtc = DTHDTC
  )

This call would return the input dataset with the columns DTHDT added and, by default, the associated date imputation flag (DTHDTF) populated with the controlled terminology outlined in the ADaM IG for date imputations. If the imputation flag is not required, the user must set the argument flag_imputation to “none”.

If imputation is needed and the date is to be imputed to the first day of the month/year the call would be:

adsl <- adsl %>%
  derive_vars_dt(
    new_vars_prefix = "DTH",
    dtc = DTHDTC,
    date_imputation = "first"
  )

See also Date and Time Imputation.

Cause of Death (DTHCAUS)

The cause of death DTHCAUS can be derived using the function derive_var_dthcaus().

Since the cause of death could be collected/mapped in different domains (e.g. DS, AE, DD), it is important the user specifies the right source(s) to derive the cause of death from.

For example, if the date of death is collected in the AE form when the AE is Fatal, the cause of death would be set to the preferred term (AEDECOD) of that Fatal AE, while if the date of death is collected in the DS form, the cause of death would be set to the disposition term (DSTERM). To achieve this, the dthcaus_source() objects must be specified and defined such as it fits the study requirement.

dthcaus_source() specifications:

• dataset_name: the name of the dataset where to search for death information,
• filter: the condition to define death,
• date: the date of death,
• mode: first or last to select the first/last date of death if multiple dates are collected,
• dthcaus: variable or text used to populate DTHCAUS.
• traceability_vars: whether the traceability variables need to be added (e.g source domain, sequence, variable)

An example call to define the sources would be:

src_ae <- dthcaus_source(
  dataset_name = "ae",
  filter = AEOUT == "FATAL",
  date = AESTDTM,
  mode = "first",
  dthcaus = AEDECOD
)

src_ds <- dthcaus_source(
  dataset_name = "ds",
  filter = DSDECOD == "DEATH" & grepl("DEATH DUE TO", DSTERM),
  date = DSSTDT,
  mode = "first",
  dthcaus = "Death in DS"
)

Once the sources are defined, the function derive_var_dthcaus() can be used to derive DTHCAUS:

ae_ext <- derive_vars_dtm(
  ae,
  dtc = AESTDTC,
  new_vars_prefix = "AEST",
  highest_imputation = "M",
  flag_imputation = "none"
)

adsl <- adsl %>%
  derive_var_dthcaus(src_ae, src_ds, source_datasets = list(ae = ae_ext, ds = ds_ext))

The function also offers the option to add some traceability variables (e.g. DTHDOM would store the domain where the date of death is collected, and DTHSEQ would store the xxSEQ value of that domain). To add them, the traceability_vars argument must be added to the dthcaus_source() arguments:

src_ae <- dthcaus_source(
  dataset_name = "ae",
  filter = AEOUT == "FATAL",
  date = AESTDTM,
  mode = "first",
  dthcaus = AEDECOD,
  traceability_vars = vars(DTHDOM = "AE", DTHSEQ = AESEQ)
)

src_ds <- dthcaus_source(
  dataset_name = "ds",
  filter = DSDECOD == "DEATH" & grepl("DEATH DUE TO", DSTERM),
  date = DSSTDT,
  mode = "first",
  dthcaus = DSTERM,
  traceability_vars = vars(DTHDOM = "DS", DTHSEQ = DSSEQ)
)
adsl <- adsl %>%
  select(-DTHCAUS) %>% # remove it before deriving it again
  derive_var_dthcaus(src_ae, src_ds, source_datasets = list(ae = ae_ext, ds = ds_ext))

Duration Relative to Death

The function derive_vars_duration() can be used to derive duration relative to death like the Relative Day of Death (DTHADY) or the numbers of days from last dose to death (LDDTHELD).

Example calls:

• Relative Day of Death

adsl <- adsl %>%
  derive_vars_duration(
    new_var = DTHADY,
    start_date = TRTSDT,
    end_date = DTHDT
  )

• Elapsed Days from Last Dose to Death

adsl <- adsl %>%
  derive_vars_duration(
    new_var = LDDTHELD,
    start_date = TRTEDT,
    end_date = DTHDT,
    add_one = FALSE
  )

Grouping (e.g. AGEGR1 or REGION1)

Numeric and categorical variables (AGE, RACE, COUNTRY, etc.) may need to be grouped to perform the required analysis. {admiral} does not currently have functionality to assist with all required groupings. Some functions exist for age grouping according to FDA or EMA conventions. For others, the user can create his/her own function to meet his/her study requirement.

To derive AGEGR1 as categorized AGE in < 18, 18-65, >= 65 (FDA convention):

adsl <- adsl %>%
  derive_var_agegr_fda(
    age_var = AGE,
    new_var = AGEGR1
  )
#> Warning: `derive_var_agegr_ema()` was deprecated in admiral 0.8.0.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

However for example if

• AGEGR2 would categorize AGE in < 65, >= 65,
• REGION1 would categorize COUNTRY in North America, Rest of the World,

the user defined function(s) would be like:

format_agegr2 <- function(var_input) {
  case_when(
    var_input < 65 ~ "< 65",
    var_input >= 65 ~ ">= 65",
    TRUE ~ NA_character_
  )
}

format_region1 <- function(var_input) {
  case_when(
    var_input %in% c("CAN", "USA") ~ "North America",
    !is.na(var_input) ~ "Rest of the World",
    TRUE ~ "Missing"
  )
}

These functions are then used in a mutate() statement to derive the required grouping variables:

adsl <- adsl %>%
  mutate(
    AGEGR2 = format_agegr2(AGE),
    REGION1 = format_region1(COUNTRY)
  )

Population Flags (e.g. SAFFL)

Since the populations flags are mainly company/study specific no dedicated functions are provided, but in most cases they can easily be derived using derive_var_merged_exist_flag.

An example of an implementation could be:

adsl <- adsl %>%
  derive_var_merged_exist_flag(
    dataset_add = ex,
    by_vars = vars(STUDYID, USUBJID),
    new_var = SAFFL,
    condition = (EXDOSE > 0 | (EXDOSE == 0 & str_detect(EXTRT, "PLACEBO")))
  )