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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

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)
USUBJID RFSTDTC COUNTRY AGE SEX RACE ETHNIC ARM ACTARM
01-701-1015 2014-01-02 USA 63 F WHITE HISPANIC OR LATINO Placebo Placebo
01-701-1023 2012-08-05 USA 64 M WHITE HISPANIC OR LATINO Placebo Placebo
01-701-1028 2013-07-19 USA 71 M WHITE NOT HISPANIC OR LATINO Xanomeline High Dose Xanomeline High Dose
01-701-1033 2014-03-18 USA 74 M WHITE NOT HISPANIC OR LATINO Xanomeline Low Dose Xanomeline Low Dose
01-701-1034 2014-07-01 USA 77 F WHITE NOT HISPANIC OR LATINO Xanomeline High Dose Xanomeline High Dose
01-701-1047 2013-02-12 USA 85 F WHITE NOT HISPANIC OR LATINO Placebo Placebo
01-701-1057 NA USA 59 F WHITE HISPANIC OR LATINO Screen Failure Screen Failure
01-701-1097 2014-01-01 USA 68 M WHITE NOT HISPANIC OR LATINO Xanomeline Low Dose Xanomeline Low Dose
01-701-1111 2012-09-07 USA 81 F WHITE NOT HISPANIC OR LATINO Xanomeline Low Dose Xanomeline Low Dose
01-701-1115 2012-11-30 USA 84 M WHITE NOT HISPANIC OR LATINO Xanomeline Low Dose Xanomeline Low Dose

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()
USUBJID RFSTDTC TRTSDTM TRTSDT TRTEDTM TRTEDT TRTDURD
01-701-1015 2014-01-02 2014-01-02 2014-01-02 2014-07-02 23:59:59 2014-07-02 182
01-701-1023 2012-08-05 2012-08-05 2012-08-05 2012-09-01 23:59:59 2012-09-01 28
01-701-1028 2013-07-19 2013-07-19 2013-07-19 2014-01-14 23:59:59 2014-01-14 180
01-701-1033 2014-03-18 2014-03-18 2014-03-18 2014-03-31 23:59:59 2014-03-31 14
01-701-1034 2014-07-01 2014-07-01 2014-07-01 2014-12-30 23:59:59 2014-12-30 183
01-701-1047 2013-02-12 2013-02-12 2013-02-12 2013-03-09 23:59:59 2013-03-09 26
01-701-1057 NA NA NA NA NA NA
01-701-1097 2014-01-01 2014-01-01 2014-01-01 2014-07-09 23:59:59 2014-07-09 190
01-701-1111 2012-09-07 2012-09-07 2012-09-07 2012-09-16 23:59:59 2012-09-16 10
01-701-1115 2012-11-30 2012-11-30 2012-11-30 2013-01-23 23:59:59 2013-01-23 55

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:

USUBJID DSCAT DSDECOD DSTERM DSSTDT DSSTDTC
01-701-1015 PROTOCOL MILESTONE RANDOMIZED RANDOMIZED 2014-01-02 2014-01-02
01-701-1015 DISPOSITION EVENT COMPLETED PROTOCOL COMPLETED 2014-07-02 2014-07-02
01-701-1015 OTHER EVENT FINAL LAB VISIT FINAL LAB VISIT 2014-07-02 2014-07-02
01-701-1023 PROTOCOL MILESTONE RANDOMIZED RANDOMIZED 2012-08-05 2012-08-05
01-701-1023 DISPOSITION EVENT ADVERSE EVENT ADVERSE EVENT 2012-09-02 2012-09-02
01-701-1023 OTHER EVENT FINAL LAB VISIT FINAL LAB VISIT 2012-09-02 2012-09-02
01-701-1023 OTHER EVENT FINAL RETRIEVAL VISIT FINAL RETRIEVAL VISIT 2013-02-18 2013-02-18
01-701-1028 PROTOCOL MILESTONE RANDOMIZED RANDOMIZED 2013-07-19 2013-07-19
01-701-1028 DISPOSITION EVENT COMPLETED PROTOCOL COMPLETED 2014-01-14 2014-01-14
01-701-1028 OTHER EVENT FINAL LAB VISIT FINAL LAB VISIT 2014-01-14 2014-01-14

The adsl dataset:

USUBJID EOSDT
01-701-1015 2014-07-02
01-701-1023 2012-09-02
01-701-1028 2014-01-14
01-701-1033 2014-04-14
01-701-1034 2014-12-30
01-701-1047 2013-03-29
01-701-1057 NA
01-701-1097 2014-07-09
01-701-1111 2012-09-17
01-701-1115 2013-01-23

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")
  )
USUBJID EOSDT EOSSTT
01-701-1015 2014-07-02 COMPLETED
01-701-1023 2012-09-02 DISCONTINUED
01-701-1028 2014-01-14 COMPLETED
01-701-1033 2014-04-14 DISCONTINUED
01-701-1034 2014-12-30 COMPLETED
01-701-1047 2013-03-29 DISCONTINUED
01-701-1057 NA NA
01-701-1097 2014-07-09 COMPLETED
01-701-1111 2012-09-17 DISCONTINUED
01-701-1115 2013-01-23 DISCONTINUED

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))
  )
USUBJID EOSDT EOSSTT DCSREAS DCSREASP
01-701-1015 2014-07-02 COMPLETED NA NA
01-701-1023 2012-09-02 DISCONTINUED ADVERSE EVENT ADVERSE EVENT
01-701-1028 2014-01-14 COMPLETED NA NA
01-701-1033 2014-04-14 DISCONTINUED STUDY TERMINATED BY SPONSOR SPONSOR DECISION (STUDY OR PATIENT DISCONTINUED BY THE SPONSOR)
01-701-1034 2014-12-30 COMPLETED NA NA
01-701-1047 2013-03-29 DISCONTINUED ADVERSE EVENT ADVERSE EVENT
01-701-1057 NA NA NA NA
01-701-1097 2014-07-09 COMPLETED NA NA
01-701-1111 2012-09-17 DISCONTINUED ADVERSE EVENT ADVERSE EVENT
01-701-1115 2013-01-23 DISCONTINUED ADVERSE EVENT ADVERSE EVENT

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"
  )
USUBJID EOSDT EOSSTT DCSREAS DCSREASP
01-701-1015 2014-07-02 COMPLETED NA NA
01-701-1023 2012-09-02 DISCONTINUED ADVERSE EVENT NA
01-701-1028 2014-01-14 COMPLETED NA NA
01-701-1033 2014-04-14 DISCONTINUED STUDY TERMINATED BY SPONSOR NA
01-701-1034 2014-12-30 COMPLETED NA NA
01-701-1047 2013-03-29 DISCONTINUED ADVERSE EVENT NA
01-701-1057 NA NA NA NA
01-701-1097 2014-07-09 COMPLETED NA NA
01-701-1111 2012-09-17 DISCONTINUED ADVERSE EVENT NA
01-701-1115 2013-01-23 DISCONTINUED ADVERSE EVENT NA

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.

USUBJID RANDDT
01-701-1015 2014-01-02
01-701-1023 2012-08-05
01-701-1028 2013-07-19
01-701-1033 2014-03-18
01-701-1034 2014-07-01
01-701-1047 2013-02-12
01-701-1057 NA
01-701-1097 2014-01-01
01-701-1111 2012-09-07
01-701-1115 2012-11-30

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
  )
USUBJID TRTEDT DTHDTC DTHDT DTHFL
01-701-1211 2013-01-12 2013-01-14 2013-01-14 Y
01-701-1442 2014-04-26 NA NA NA
01-704-1445 2014-11-01 2014-11-01 2014-11-01 Y
01-705-1058 NA NA NA NA
01-708-1347 2013-06-18 NA NA NA
01-710-1083 2013-08-01 2013-08-02 2013-08-02 Y
01-710-1235 2013-03-27 NA NA NA
01-715-1207 2013-05-27 NA NA NA
01-718-1172 2013-11-29 NA NA NA

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)
  )
USUBJID EOSDT DTHDTC DTHDT DTHCAUS
01-701-1211 2013-01-14 2013-01-14 2013-01-14 SUDDEN DEATH
01-704-1445 2014-11-01 2014-11-01 2014-11-01 COMPLETED SUICIDE
01-710-1083 2013-08-02 2013-08-02 2013-08-02 MYOCARDIAL INFARCTION

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)
  )
USUBJID TRTEDT DTHDTC DTHDT DTHCAUS DTHDOM DTHSEQ
01-701-1211 2013-01-12 2013-01-14 2013-01-14 SUDDEN DEATH AE 9
01-704-1445 2014-11-01 2014-11-01 2014-11-01 COMPLETED SUICIDE AE 1
01-710-1083 2013-08-01 2013-08-02 2013-08-02 MYOCARDIAL INFARCTION AE 1

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
  )
USUBJID TRTEDT DTHDTC DTHDT DTHCAUS DTHADY LDDTHELD
01-701-1211 2013-01-12 2013-01-14 2013-01-14 SUDDEN DEATH 61 2
01-704-1445 2014-11-01 2014-11-01 2014-11-01 COMPLETED SUICIDE 175 0
01-710-1083 2013-08-01 2013-08-02 2013-08-02 MYOCARDIAL INFARCTION 12 1

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)
  )
USUBJID TRTEDT DTHDTC LSTALVDT
01-701-1015 2014-07-02 NA 2014-07-02
01-701-1023 2012-09-01 NA 2012-09-02
01-701-1028 2014-01-14 NA 2014-01-14
01-701-1033 2014-03-31 NA 2014-04-14
01-701-1034 2014-12-30 NA 2014-12-30
01-701-1047 2013-03-09 NA 2013-04-07
01-701-1097 2014-07-09 NA 2014-07-09
01-701-1111 2012-09-16 NA 2012-09-17
01-701-1115 2013-01-23 NA 2013-01-23
01-701-1118 2014-09-09 NA 2014-09-09

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)
  )
USUBJID TRTEDT DTHDTC LSTALVDT LALVDOM LALVSEQ LALVVAR
01-701-1015 2014-07-02 NA 2014-07-02 ADSL NA TRTEDTM
01-701-1023 2012-09-01 NA 2012-09-02 LB 107 LBDTC
01-701-1028 2014-01-14 NA 2014-01-14 ADSL NA TRTEDTM
01-701-1033 2014-03-31 NA 2014-04-14 LB 107 LBDTC
01-701-1034 2014-12-30 NA 2014-12-30 ADSL NA TRTEDTM
01-701-1047 2013-03-09 NA 2013-04-07 LB 134 LBDTC
01-701-1097 2014-07-09 NA 2014-07-09 ADSL NA TRTEDTM
01-701-1111 2012-09-16 NA 2012-09-17 LB 73 LBDTC
01-701-1115 2013-01-23 NA 2013-01-23 ADSL NA TRTEDTM
01-701-1118 2014-09-09 NA 2014-09-09 ADSL NA TRTEDTM

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)
  )
USUBJID AGE SEX COUNTRY AGEGR1 REGION1
01-701-1015 63 F USA 18-64 North America
01-701-1023 64 M USA 18-64 North America
01-701-1028 71 M USA >64 North America
01-701-1033 74 M USA >64 North America
01-701-1034 77 F USA >64 North America
01-701-1047 85 F USA >64 North America
01-701-1057 59 F USA 18-64 North America
01-701-1097 68 M USA >64 North America
01-701-1111 81 F USA >64 North America
01-701-1115 84 M USA >64 North America

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")))
  )
USUBJID TRTSDT ARM ACTARM SAFFL
01-701-1015 2014-01-02 Placebo Placebo Y
01-701-1023 2012-08-05 Placebo Placebo Y
01-701-1028 2013-07-19 Xanomeline High Dose Xanomeline High Dose Y
01-701-1033 2014-03-18 Xanomeline Low Dose Xanomeline Low Dose Y
01-701-1034 2014-07-01 Xanomeline High Dose Xanomeline High Dose Y
01-701-1047 2013-02-12 Placebo Placebo Y
01-701-1057 NA Screen Failure Screen Failure NA
01-701-1097 2014-01-01 Xanomeline Low Dose Xanomeline Low Dose Y
01-701-1111 2012-09-07 Xanomeline Low Dose Xanomeline Low Dose Y
01-701-1115 2012-11-30 Xanomeline Low Dose Xanomeline Low Dose Y

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:

See also Generic Functions.

Add Labels and Attributes

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

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")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.