Creating ADSL

Introduction

This article describes creating an ADSL ADaM. Examples are currently presented and tested using DM, EX , AE, LB and DS SDTM domains. However, other domains could be used.

Note: All examples assume CDISC SDTM and/or ADaM format as input unless otherwise specified.

Programming Flow

• Read in Data
• Derive Period, Subperiod, and Phase Variables (e.g. APxxSDT, APxxEDT, …)
• Derive Treatment Variables (TRT0xP, TRT0xA)
• Derive/Impute Numeric Treatment Date/Time and Duration (TRTSDT, TRTEDT, TRTDURD)
• Derive Disposition Variables
  • Disposition Dates (e.g. EOSDT)
  • Disposition Status (e.g. EOSTT)
  • Disposition Reason(s) (e.g. DCSREAS, DCSREASP)
  • Randomization Date (RANDDT)
• Derive Death Variables
  • Death Date (DTHDT)
  • Cause of Death (DTHCAUS)
  • Duration Relative to Death
• Derive Last Known Date Alive (LSTALVDT)
• Derive Groupings and Populations
  • Grouping (e.g. AGEGR1 or REGION1)
  • Population Flags (e.g. SAFFL)
• Derive Other Variables
• Add Labels and Attributes

Read in Data

To start, all data frames needed for the creation of ADSL should be read into the environment. This will be a company specific process. Some of the data frames needed may be DM, EX, DS, AE, and LB.

For example purpose, the CDISC Pilot SDTM datasets—which are included in pharmaversesdtm—are used.

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

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

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)

The DM domain is used as the basis for ADSL:

adsl <- dm %>%
  select(-DOMAIN)

Derive Period, Subperiod, and Phase Variables (e.g. APxxSDT, APxxEDT, …)

See the “Visit and Period Variables” vignette for more information.

If the variables are not derived based on a period reference dataset, they may be derived at a later point of the flow. For example, phases like “Treatment Phase” and “Follow up” could be derived based on treatment start and end date.

Derive Treatment Variables (TRT0xP, TRT0xA)

The mapping of the treatment variables is left to the ADaM programmer. An example mapping for a study without periods may be:

adsl <- dm %>%
  mutate(TRT01P = ARM, TRT01A = ACTARM)

For studies with periods see the “Visit and Period Variables” vignette.

Derive/Impute Numeric Treatment Date/Time and Duration (TRTSDTM, TRTEDTM, TRTDURD)

The function derive_vars_merged() can be used to derive the treatment start and end date/times using the ex domain. A pre-processing step for ex is required to convert the variable EXSTDTC and EXSTDTC to datetime variables and impute missing date or time components. Conversion and imputation is done by derive_vars_dtm().

Example calls:

# 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 <- adsl %>%
  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_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)
  )

This call returns the original data frame with the column TRTSDTM, TRTSTMF, TRTEDTM, and TRTETMF added. Exposure observations with incomplete date and zero doses of non placebo treatments are ignored. Missing time parts are imputed as first or last for start and end date respectively.

The datetime variables returned can be converted to dates using the derive_vars_dtm_to_dt() function.

adsl <- adsl %>%
  derive_vars_dtm_to_dt(source_vars = exprs(TRTSDTM, TRTEDTM))

Now, that TRTSDT and TRTEDT are derived, the function derive_var_trtdurd() can be used to calculate the Treatment duration (TRTDURD).

adsl <- adsl %>%
  derive_var_trtdurd()

Derive Disposition Variables

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(). The DS dataset is extended by the DSSTDT variable because the date is required by other derivations, e.g., RANDDT as well. Then the relevant disposition date is selected by adjusting the filter_add argument.

To add the End of Study date (EOSDT) to the input dataset, 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 = exprs(STUDYID, USUBJID),
    new_vars = exprs(EOSDT = DSSTDT),
    filter_add = DSCAT == "DISPOSITION EVENT" & DSDECOD != "SCREEN FAILURE"
  )

The ds_ext dataset:

The adsl dataset:

The derive_vars_dt() function allows to impute partial dates as well. If imputation is needed and missing days are to be imputed to the first of the month and missing months to the first month of the year, set highest_imputation = "M".

Disposition Status (e.g. EOSSTT)

The function derive_vars_merged() can be used to derive the End of Study status (EOSSTT) based on DSCAT and DSDECOD from DS. The relevant observations are selected by adjusting the filter_add argument. A function mapping DSDECOD values to EOSSTT values can be defined and used in the new_vars argument. The mapping for the call below is

• "COMPLETED" if DSDECOD == "COMPLETED"
• NA_character_ if DSDECOD is "SCREEN FAILURE"
• "DISCONTINUED" otherwise

Example function format_eosstt():

format_eosstt <- function(x) {
  case_when(
    x %in% c("COMPLETED") ~ "COMPLETED",
    x %in% c("SCREEN FAILURE") ~ NA_character_,
    TRUE ~ "DISCONTINUED"
  )
}

The customized mapping function format_eosstt() can now be passed to the main function. For subjects without a disposition event the end of study status is set to "ONGOING" by specifying the missing_values argument.

adsl <- adsl %>%
  derive_vars_merged(
    dataset_add = ds,
    by_vars = exprs(STUDYID, USUBJID),
    filter_add = DSCAT == "DISPOSITION EVENT",
    new_vars = exprs(EOSSTT = format_eosstt(DSDECOD)),
    missing_values = exprs(EOSSTT = "ONGOING")
  )

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

If the derivation must be changed, the user can create his/her own function to map DSDECOD to a suitable EOSSTT value.

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_merged() can be used to derive a disposition reason (along with the details, if required) at a specific timepoint. The relevant observations are selected by adjusting the filter_add argument.

To derive the End of Study reason(s) (DCSREAS and DCSREASP), the function will map DCSREAS as DSDECOD, and DCSREASP as DSTERM if DSDECOD is not "COMPLETED", "SCREEN FAILURE", or NA, NA otherwise.

adsl <- adsl %>%
  derive_vars_merged(
    dataset_add = ds,
    by_vars = exprs(USUBJID),
    new_vars = exprs(DCSREAS = DSDECOD, DCSREASP = DSTERM),
    filter_add = DSCAT == "DISPOSITION EVENT" &
      !(DSDECOD %in% c("SCREEN FAILURE", "COMPLETED", NA))
  )

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

If the derivation must be changed, the user can define that derivation in the filter_add argument of the function to map DSDECOD and DSTERM to a suitable DCSREAS/DCSREASP value.

The call below maps DCSREAS and DCREASP as follows:

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

adsl <- adsl %>%
  derive_vars_merged(
    dataset_add = ds,
    by_vars = exprs(USUBJID),
    new_vars = exprs(DCSREAS = DSDECOD),
    filter_add = DSCAT == "DISPOSITION EVENT" &
      DSDECOD %notin% c("SCREEN FAILURE", "COMPLETED", NA)
  ) %>%
  derive_vars_merged(
    dataset_add = ds,
    by_vars = exprs(USUBJID),
    new_vars = exprs(DCSREASP = DSTERM),
    filter_add = DSCAT == "DISPOSITION EVENT" & DSDECOD %in% "OTHER"
  )

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 = exprs(STUDYID, USUBJID),
    new_vars = exprs(RANDDT = DSSTDT)
  )

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

Derive Death Variables

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_vars_extreme_event().

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 event() objects within derive_vars_extreme_event() must be specified and defined such that they fit the study requirement.

An example call to derive_vars_extreme_event() would be:

adsl <- adsl %>%
  derive_vars_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    events = list(
      event(
        dataset_name = "ae",
        condition = AEOUT == "FATAL",
        set_values_to = exprs(DTHCAUS = AEDECOD),
      ),
      event(
        dataset_name = "ds",
        condition = DSDECOD == "DEATH" & grepl("DEATH DUE TO", DSTERM),
        set_values_to = exprs(DTHCAUS = DSTERM),
      )
    ),
    source_datasets = list(ae = ae, ds = ds),
    tmp_event_nr_var = event_nr,
    order = exprs(event_nr),
    mode = "first",
    new_vars = exprs(DTHCAUS)
  )

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). The traceability variables should be added to the event() calls and included in the new_vars parameter of derive_vars_extreme_event().

adsl <- adsl %>%
  select(-DTHCAUS) %>% # remove it before deriving it again
  derive_vars_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    events = list(
      event(
        dataset_name = "ae",
        condition = AEOUT == "FATAL",
        set_values_to = exprs(DTHCAUS = AEDECOD, DTHDOM = "AE", DTHSEQ = AESEQ),
      ),
      event(
        dataset_name = "ds",
        condition = DSDECOD == "DEATH" & grepl("DEATH DUE TO", DSTERM),
        set_values_to = exprs(DTHCAUS = DSTERM, DTHDOM = "DS", DTHSEQ = DSSEQ),
      )
    ),
    source_datasets = list(ae = ae, ds = ds),
    tmp_event_nr_var = event_nr,
    order = exprs(event_nr),
    mode = "first",
    new_vars = exprs(DTHCAUS, DTHDOM, DTHSEQ)
  )

Following the derivation of DTHCAUS and related traceability variables, it is then possible to derive grouping variables such as death categories (DTHCGRx) using standard tidyverse code.

adsl <- adsl %>%
  mutate(DTHCGR1 = case_when(
    is.na(DTHDOM) ~ NA_character_,
    DTHDOM == "AE" ~ "ADVERSE EVENT",
    str_detect(DTHCAUS, "(PROGRESSIVE DISEASE|DISEASE RELAPSE)") ~ "PROGRESSIVE DISEASE",
    TRUE ~ "OTHER"
  ))

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
  )

Derive the Last Date Known Alive (LSTALVDT)

Similarly as for the cause of death (DTHCAUS), the last known alive date (LSTALVDT) can be derived from multiples sources using derive_vars_extreme_event().

An example could be (DTC dates are converted to numeric dates imputing missing day and month to the first):

adsl <- adsl %>%
  derive_vars_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    events = list(
      event(
        dataset_name = "ae",
        order = exprs(AESTDTC, AESEQ),
        condition = !is.na(AESTDTC),
        set_values_to = exprs(
          LSTALVDT = convert_dtc_to_dt(AESTDTC, highest_imputation = "M"),
          seq = AESEQ
        ),
      ),
      event(
        dataset_name = "ae",
        order = exprs(AEENDTC, AESEQ),
        condition = !is.na(AEENDTC),
        set_values_to = exprs(
          LSTALVDT = convert_dtc_to_dt(AEENDTC, highest_imputation = "M"),
          seq = AESEQ
        ),
      ),
      event(
        dataset_name = "lb",
        order = exprs(LBDTC, LBSEQ),
        condition = !is.na(LBDTC),
        set_values_to = exprs(
          LSTALVDT = convert_dtc_to_dt(LBDTC, highest_imputation = "M"),
          seq = LBSEQ
        ),
      ),
      event(
        dataset_name = "adsl",
        condition = !is.na(TRTEDT),
        set_values_to = exprs(LSTALVDT = TRTEDT, seq = 0),
      )
    ),
    source_datasets = list(ae = ae, lb = lb, adsl = adsl),
    tmp_event_nr_var = event_nr,
    order = exprs(LSTALVDT, seq, event_nr),
    mode = "last",
    new_vars = exprs(LSTALVDT)
  )

Traceability variables can be added by specifying the variables in the set_values_to parameter of the event() function.

adsl <- adsl %>%
  select(-LSTALVDT) %>% # created in the previous call
  derive_vars_extreme_event(
    by_vars = exprs(STUDYID, USUBJID),
    events = list(
      event(
        dataset_name = "ae",
        order = exprs(AESTDTC, AESEQ),
        condition = !is.na(AESTDTC),
        set_values_to = exprs(
          LSTALVDT = convert_dtc_to_dt(AESTDTC, highest_imputation = "M"),
          LALVSEQ = AESEQ,
          LALVDOM = "AE",
          LALVVAR = "AESTDTC"
        ),
      ),
      event(
        dataset_name = "ae",
        order = exprs(AEENDTC, AESEQ),
        condition = !is.na(AEENDTC),
        set_values_to = exprs(
          LSTALVDT = convert_dtc_to_dt(AEENDTC, highest_imputation = "M"),
          LALVSEQ = AESEQ,
          LALVDOM = "AE",
          LALVVAR = "AEENDTC"
        ),
      ),
      event(
        dataset_name = "lb",
        order = exprs(LBDTC, LBSEQ),
        condition = !is.na(LBDTC),
        set_values_to = exprs(
          LSTALVDT = convert_dtc_to_dt(LBDTC, highest_imputation = "M"),
          LALVSEQ = LBSEQ,
          LALVDOM = "LB",
          LALVVAR = "LBDTC"
        ),
      ),
      event(
        dataset_name = "adsl",
        condition = !is.na(TRTEDT),
        set_values_to = exprs(LSTALVDT = TRTEDT, LALVSEQ = NA_integer_, LALVDOM = "ADSL", LALVVAR = "TRTEDTM"),
      )
    ),
    source_datasets = list(ae = ae, lb = lb, adsl = adsl),
    tmp_event_nr_var = event_nr,
    order = exprs(LSTALVDT, LALVSEQ, event_nr),
    mode = "last",
    new_vars = exprs(LSTALVDT, LALVSEQ, LALVDOM, LALVVAR)
  )

Derive Groupings and Populations

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. So, the user will often need to create his/her own function to meet his/her study requirement.

For example, if

• AGEGR1 is required to categorize AGE into <18, 18-64 and >64, or
• REGION1 is required to categorize COUNTRY in North America, Rest of the World,

the user defined functions would look like the following:

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

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(
    AGEGR1 = format_agegr1(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 = exprs(STUDYID, USUBJID),
    new_var = SAFFL,
    condition = (EXDOSE > 0 | (EXDOSE == 0 & str_detect(EXTRT, "PLACEBO")))
  )

Derive Other Variables

The users can add specific code to cover their need for the analysis.

The following functions are helpful for many ADSL derivations:

• derive_vars_merged() - Merge Variables from a Dataset to the Input Dataset
• derive_var_merged_exist_flag() - Merge an Existence Flag
• derive_var_merged_summary() - Merge Summary Variables

See also Generic Functions.

Add Labels and Attributes

Adding labels and attributes for SAS transport files is supported by the following packages:

• metacore: establish a common foundation for the use of metadata within an R session.

• metatools: enable the use of metacore objects. Metatools can be used to build datasets or enhance columns in existing datasets as well as checking datasets against the metadata.

• xportr: functionality to associate all metadata information to a local R data frame, perform data set level validation checks and convert into a transport v5 file(xpt).

NOTE: All these packages are in the experimental phase, but the vision is to have them associated with an End to End pipeline under the umbrella of the pharmaverse. An example of applying metadata and perform associated checks can be found at the pharmaverse E2E example.

Example Script

ADaM	Sourcing Command
ADSL	use_ad_template("ADSL")