# 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 `{admiral.test}`—are used.

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

    data("admiral_dm")
    data("admiral_ds")
    data("admiral_ex")
    data("admiral_ae")
    data("admiral_lb")

    dm <- convert_blanks_to_na(admiral_dm)
    ds <- convert_blanks_to_na(admiral_ds)
    ex <- convert_blanks_to_na(admiral_ex)
    ae <- convert_blanks_to_na(admiral_ae)
    lb <- convert_blanks_to_na(admiral_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()`. 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 = exprs(STUDYID, USUBJID),
        new_vars = exprs(EOSDT = DSSTDT),
        filter_add = DSCAT == "DISPOSITION EVENT" & DSDECOD != "SCREEN FAILURE"
      )

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

We would get :

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

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_merged_cat()` 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`), the function expects a mapping derivation for the `cat_fun` argument. The mapping derivation for the call below is

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

Example function `format_eosstt()`:

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

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

    adsl <- adsl %>%
      derive_var_merged_cat(
        dataset_add = ds,
        by_vars = exprs(STUDYID, USUBJID),
        filter_add = DSCAT == "DISPOSITION EVENT",
        new_var = EOSSTT,
        source_var = DSDECOD,
        cat_fun = format_eosstt,
        missing_value = "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 and pass it to the `cat_fun` argument of the function (`cat_fun = new_mapping`) 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 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 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 %notin% 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
--- | --- | --- | --- | ---
01-701-1015 | 2014-07-02 | NA | NA | NA
01-701-1023 | 2012-09-01 | NA | NA | NA
01-701-1028 | 2014-01-14 | NA | NA | NA
01-701-1033 | 2014-03-31 | NA | NA | NA
01-701-1034 | 2014-12-30 | NA | NA | NA
01-701-1047 | 2013-03-09 | NA | NA | NA
01-701-1057 | NA | NA | NA | NA
01-701-1097 | 2014-07-09 | NA | NA | NA
01-701-1111 | 2012-09-16 | NA | NA | NA
01-701-1115 | 2013-01-23 | 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_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
    )

USUBJID | AESTDTC | AEENDTC | AEDECOD | AEOUT
--- | --- | --- | --- | ---
01-701-1211 | 2013-01-14 | 2013-01-14 | SUDDEN DEATH | FATAL
01-704-1445 | 2014-10-31 | 2014-10-31 | COMPLETED SUICIDE | FATAL
01-710-1083 | 2013-08-02 | 2013-08-02 | MYOCARDIAL INFARCTION | FATAL

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

USUBJID | DSDECOD | DSTERM | DSSTDTC
--- | --- | --- | ---
01-701-1211 | DEATH | DEATH | 2013-01-14
01-704-1445 | DEATH | DEATH | 2014-11-01
01-710-1083 | DEATH | DEATH | 2013-08-02

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))

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). 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 = exprs(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 = exprs(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))

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

### 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 and the user must ensure the sources (`date_source()`) are correctly defined.

`date_source()` specifications:

- `dataset_name`: the name of the dataset where to search for date information,
- `filter`: the filter to apply on the datasets,
- `date`: the date of interest,
- `date_imputation`: whether and how to impute partial dates,
- `traceability_vars`: whether the traceability variables need to be added (e.g source domain, sequence, variable)

An example could be :

    ae_start_date <- date_source(
      dataset_name = "ae",
      date = AESTDT
    )
    ae_end_date <- date_source(
      dataset_name = "ae",
      date = AEENDT
    )
    lb_date <- date_source(
      dataset_name = "lb",
      date = LBDT,
      filter = !is.na(LBDT)
    )
    trt_end_date <- date_source(
      dataset_name = "adsl",
      date = TRTEDT
    )

Once the sources are defined, the function `derive_var_extreme_dt()` can be used to derive `LSTALVDT`:

    # impute AE start and end date to first
    ae_ext <- ae %>%
      derive_vars_dt(
        dtc = AESTDTC,
        new_vars_prefix = "AEST",
        highest_imputation = "M"
      ) %>%
      derive_vars_dt(
        dtc = AEENDTC,
        new_vars_prefix = "AEEN",
        highest_imputation = "M"
      )

    # impute LB date to first
    lb_ext <- derive_vars_dt(
      lb,
      dtc = LBDTC,
      new_vars_prefix = "LB",
      highest_imputation = "M"
    )

    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_ext, adsl = adsl, lb = lb_ext),
        mode = "last"
      )

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

Similarly to `dthcaus_source()`, the traceability variables can be added by specifying the `traceability_vars` argument in `date_source()`.

    ae_start_date <- date_source(
      dataset_name = "ae",
      date = AESTDT,
      traceability_vars = exprs(LALVDOM = "AE", LALVSEQ = AESEQ, LALVVAR = "AESTDTC")
    )
    ae_end_date <- date_source(
      dataset_name = "ae",
      date = AEENDT,
      traceability_vars = exprs(LALVDOM = "AE", LALVSEQ = AESEQ, LALVVAR = "AEENDTC")
    )
    lb_date <- date_source(
      dataset_name = "lb",
      date = LBDT,
      filter = !is.na(LBDT),
      traceability_vars = exprs(LALVDOM = "LB", LALVSEQ = LBSEQ, LALVVAR = "LBDTC")
    )
    trt_end_date <- date_source(
      dataset_name = "adsl",
      date = TRTEDTM,
      traceability_vars = exprs(LALVDOM = "ADSL", LALVSEQ = NA_integer_, LALVVAR = "TRTEDTM")
    )

    adsl <- adsl %>%
      select(-LSTALVDT) %>% # created in the previous call
      derive_var_extreme_dt(
        new_var = LSTALVDT,
        ae_start_date, ae_end_date, lb_date, trt_end_date,
        source_datasets = list(ae = ae_ext, adsl = adsl, lb = lb_ext),
        mode = "last"
      )

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:

- `derive_vars_merged()` - Merge Variables from a Dataset to the Input Dataset
- `derive_var_merged_exist_flag()` - Merge an Existence Flag
- `derive_var_merged_cat()` - Merge a Categorization Variable
- `derive_var_merged_character()` - Merge a Character Variable
- `derive_var_merged_summary()` - Merge a Summary Variable

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 | Sample Code
--- | ---
ADSL | ad_adsl.R