# Creating a BDS Time-to-Event ADaM

# Introduction

This article describes creating a BDS time-to-event ADaM.

The main part in programming a time-to-event dataset is the definition of the events and censoring times. `{admiral}` supports single events like death or composite events like disease progression or death. More than one source dataset can be used for the definition of the event and censoring times.

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

## Required Packages

The examples of this vignette require the following packages.

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

# Programming Workflow

- Read in Data
- Derive Parameters (`CNSR`, `ADT`, `STARTDT`)
- Derive Analysis Value (`AVAL`)
- Derive Analysis Sequence Number (`ASEQ`)
- Add ADSL Variables
- Add Labels and Attributes

## Read in Data

To start, all datasets needed for the creation of the time-to-event dataset should be read into the environment. This will be a company specific process.

For example purpose, the ADSL dataset—which is included in `{admiral}`—and the SDTM datasets from `{pharmaversesdtm}` are used.

    ae <- pharmaversesdtm::ae
    adsl <- admiral::admiral_adsl

    ae <- convert_blanks_to_na(ae)

The following code creates a minimally viable ADAE dataset to be used throughout the following examples.

    adae <- ae %>%
      left_join(adsl, by = c("STUDYID", "USUBJID")) %>%
      derive_vars_dt(
        new_vars_prefix = "AST",
        dtc = AESTDTC,
        highest_imputation = "M"
      ) %>%
      derive_vars_dt(
        new_vars_prefix = "AEN",
        dtc = AEENDTC,
        highest_imputation = "M",
        date_imputation = "last"
      ) %>%
      mutate(TRTEMFL = if_else(ASTDT >= TRTSDT &
        AENDT <= TRTEDT + days(30), "Y", NA_character_))

## Derive Parameters (`CNSR`, `ADT`, `STARTDT`)

To derive the parameter dependent variables like `CNSR`, `ADT`, `STARTDT`, `EVNTDESC`, `SRCDOM`, `PARAMCD`, … the `derive_param_tte()` function can be used. It adds one parameter to the input dataset with one observation per subject. Usually it is called several times.

For each subject it is determined if an event occurred. In the affirmative the analysis date `ADT` is set to the earliest event date. If no event occurred, the analysis date is set to the latest censoring date.

The events and censorings are defined by the `event_source()` and the `censor_source()` class respectively. It defines

- which observations (`filter` parameter) of a source dataset (`dataset_name` parameter) are potential events or censorings,
- the value of the `CNSR` variable (`censor` parameter), and
- which variable provides the date (`date` parameter).

The date can be provided as date (`*DT` variable) or datetime (`*DTM` variable).

CDISC strongly recommends `CNSR = 0` for events and positive integers for censorings. `{admiral}` enforces this recommendation. Therefore the `censor` parameter is available for `censor_source()` only. It is defaulted to `1`.

The `dataset_name` parameter expects a character value which is used as an identifier. The actual data which is used for the derivation of the parameter is provided via the `source_datasets` parameter of `derive_param_tte()`. It expects a named list of datasets. The names correspond to the identifiers specified for the `dataset_name` parameter. This allows to define events and censoring independent of the data.

### Pre-Defined Time-to-Event Source Objects

The table below shows all pre-defined `tte_source` objects which should cover the most common use cases.

`tte_source` objects

These pre-defined objects can be passed directly to `derive_param_tte()` to create a new time-to-event parameter.

    adtte <- derive_param_tte(
      dataset_adsl = adsl,
      start_date = TRTSDT,
      event_conditions = list(ae_ser_event),
      censor_conditions = list(lastalive_censor),
      source_datasets = list(adsl = adsl, adae = adae),
      set_values_to = exprs(PARAMCD = "TTAESER", PARAM = "Time to First Serious AE")
    )

USUBJID | PARAMCD | PARAM | STARTDT | ADT | CNSR
--- | --- | --- | --- | --- | ---
01-701-1015 | TTAESER | Time to First Serious AE | 2014-01-02 | 2014-07-02 | 1
01-701-1023 | TTAESER | Time to First Serious AE | 2012-08-05 | 2012-09-02 | 1
01-701-1028 | TTAESER | Time to First Serious AE | 2013-07-19 | 2014-01-14 | 1
01-701-1033 | TTAESER | Time to First Serious AE | 2014-03-18 | 2014-04-14 | 1
01-701-1034 | TTAESER | Time to First Serious AE | 2014-07-01 | 2014-12-30 | 1
01-701-1047 | TTAESER | Time to First Serious AE | 2013-02-12 | 2013-04-07 | 1
01-701-1097 | TTAESER | Time to First Serious AE | 2014-01-01 | 2014-07-09 | 1
01-701-1111 | TTAESER | Time to First Serious AE | 2012-09-07 | 2012-09-17 | 1
01-701-1115 | TTAESER | Time to First Serious AE | 2012-11-30 | 2013-01-23 | 1
01-701-1118 | TTAESER | Time to First Serious AE | 2014-03-12 | 2014-09-09 | 1

### Single Event

For example, the overall survival time could be defined from treatment start to death. Patients alive or lost to follow-up would be censored to the last alive date. The following call defines a death event based on ADSL variables.

    death <- event_source(
      dataset_name = "adsl",
      filter = DTHFL == "Y",
      date = DTHDT
    )

A corresponding censoring based on the last known alive date can be defined by the following call.

    lstalv <- censor_source(
      dataset_name = "adsl",
      date = LSTALVDT
    )

The definitions can be passed to `derive_param_tte()` to create a new time-to-event parameter.

    adtte <- derive_param_tte(
      dataset_adsl = adsl,
      source_datasets = list(adsl = adsl),
      start_date = TRTSDT,
      event_conditions = list(death),
      censor_conditions = list(lstalv),
      set_values_to = exprs(PARAMCD = "OS", PARAM = "Overall Survival")
    )

USUBJID | PARAMCD | PARAM | STARTDT | ADT | CNSR
--- | --- | --- | --- | --- | ---
01-701-1015 | OS | Overall Survival | 2014-01-02 | 2014-07-02 | 1
01-701-1023 | OS | Overall Survival | 2012-08-05 | 2012-09-02 | 1
01-701-1028 | OS | Overall Survival | 2013-07-19 | 2014-01-14 | 1
01-701-1033 | OS | Overall Survival | 2014-03-18 | 2014-04-14 | 1
01-701-1034 | OS | Overall Survival | 2014-07-01 | 2014-12-30 | 1
01-701-1047 | OS | Overall Survival | 2013-02-12 | 2013-04-07 | 1
01-701-1097 | OS | Overall Survival | 2014-01-01 | 2014-07-09 | 1
01-701-1111 | OS | Overall Survival | 2012-09-07 | 2012-09-17 | 1
01-701-1115 | OS | Overall Survival | 2012-11-30 | 2013-01-23 | 1
01-701-1118 | OS | Overall Survival | 2014-03-12 | 2014-09-09 | 1

Note that in practice for efficacy parameters you might use randomization date as the time to event origin date.

### Add Additional Information for Events and Censoring (`EVNTDESC`, `SRCVAR`, …)

To add additional information like event or censoring description (`EVNTDESC`) or source variable (`SRCVAR`) the `set_values_to` parameter can be specified in the event/censoring definition.

    # define death event #
    death <- event_source(
      dataset_name = "adsl",
      filter = DTHFL == "Y",
      date = DTHDT,
      set_values_to = exprs(
        EVNTDESC = "DEATH",
        SRCDOM = "ADSL",
        SRCVAR = "DTHDT"
      )
    )

    # define censoring at last known alive date #
    lstalv <- censor_source(
      dataset_name = "adsl",
      date = LSTALVDT,
      set_values_to = exprs(
        EVNTDESC = "LAST KNOWN ALIVE DATE",
        SRCDOM = "ADSL",
        SRCVAR = "LSTALVDT"
      )
    )

    # derive time-to-event parameter #
    adtte <- derive_param_tte(
      dataset_adsl = adsl,
      source_datasets = list(adsl = adsl),
      event_conditions = list(death),
      censor_conditions = list(lstalv),
      set_values_to = exprs(PARAMCD = "OS", PARAM = "Overall Survival")
    )

USUBJID | EVNTDESC | SRCDOM | SRCVAR | CNSR | ADT
--- | --- | --- | --- | --- | ---
01-701-1015 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-07-02
01-701-1023 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2012-09-02
01-701-1028 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-01-14
01-701-1033 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-04-14
01-701-1034 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-12-30
01-701-1047 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2013-04-07
01-701-1097 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-07-09
01-701-1111 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2012-09-17
01-701-1115 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2013-01-23
01-701-1118 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-09-09

### Handling Subjects Without Assessment

If a subject has no event and has no record meeting the censoring rule, it will not be included in the output dataset. In order to have a record for this subject in the output dataset, another `censoring_source()` object should be created to specify how those patients will be censored. Therefore the `start` censoring is defined below to achieve that subjects without data in `adrs` are censored at the start date.

The ADaM IG requires that a computed date must be accompanied by imputation flags. Thus, if the function detects a `*DTF` and/or `*TMF` variable corresponding to `start_date` then `STARTDTF` and `STARTTMF` are set automatically to the values of these variables. If a date variable from one of the event or censoring source datasets is imputed, the imputation flag can be specified for the `set_values_to` parameter in `event_source()` or `censor_source()` (see definition of the `start` censoring below).

As the CDISC pilot does not contain a `RS` dataset, the following example for progression free survival uses manually created datasets.

    View(adsl)

USUBJID | DTHFL | DTHDT | TRTSDT | TRTSDTF
--- | --- | --- | --- | ---
01 | Y | 2021-06-12 | 2021-01-01 | M
02 | N | NA | 2021-02-03 | NA
03 | Y | 2021-08-21 | 2021-08-10 | NA
04 | N | NA | 2021-02-03 | NA
05 | N | NA | 2021-04-01 | D

    View(adrs)

USUBJID | AVALC | ADT | ASEQ | PARAMCD | PARAM
--- | --- | --- | --- | --- | ---
01 | SD | 2021-01-03 | 1 | OVR | Overall Response
01 | PR | 2021-03-04 | 2 | OVR | Overall Response
01 | PD | 2021-05-05 | 3 | OVR | Overall Response
02 | PD | 2021-02-03 | 1 | OVR | Overall Response
04 | SD | 2021-02-13 | 1 | OVR | Overall Response
04 | PR | 2021-04-14 | 2 | OVR | Overall Response
04 | CR | 2021-05-15 | 3 | OVR | Overall Response

An event for progression free survival occurs if

- progression of disease is observed or
- the subject dies.

Therefore two `event_source()` objects are defined:

- `pd` for progression of disease and
- `death` for death.

Some subjects may experience both events. In this case the first one is selected by `derive_param_tte()`.

    # progressive disease event #
    pd <- event_source(
      dataset_name = "adrs",
      filter = AVALC == "PD",
      date = ADT,
      set_values_to = exprs(
        EVNTDESC = "PD",
        SRCDOM = "ADRS",
        SRCVAR = "ADT",
        SRCSEQ = ASEQ
      )
    )

    # death event #
    death <- event_source(
      dataset_name = "adsl",
      filter = DTHFL == "Y",
      date = DTHDT,
      set_values_to = exprs(
        EVNTDESC = "DEATH",
        SRCDOM = "ADSL",
        SRCVAR = "DTHDT"
      )
    )

Subjects without event must be censored at the last tumor assessment. For the censoring the `lastvisit` object is defined as all tumor assessments. Please note that it is not necessary to select the last one or exclude assessments which resulted in progression of disease. This is handled within `derive_param_tte()`.

    # last tumor assessment censoring (CNSR = 1 by default) #
    lastvisit <- censor_source(
      dataset_name = "adrs",
      date = ADT,
      set_values_to = exprs(
        EVNTDESC = "LAST TUMOR ASSESSMENT",
        SRCDOM = "ADRS",
        SRCVAR = "ADT",
        SRCSEQ = ASEQ
      )
    )

Patients without tumor assessment should be censored at the start date. Therefore the `start` object is defined with the treatment start date as censoring date. It is not necessary to exclude patient with tumor assessment in the definition of `start` because `derive_param_tte()` selects the last date across all `censor_source()` objects as censoring date.

    # start date censoring (for patients without tumor assessment) (CNSR = 2) #
    start <- censor_source(
      dataset_name = "adsl",
      date = TRTSDT,
      censor = 2,
      set_values_to = exprs(
        EVNTDESC = "TREATMENT START",
        SRCDOM = "ADSL",
        SRCVAR = "TRTSDT",
        ADTF = TRTSDTF
      )
    )

    # derive time-to-event parameter #
    adtte <- derive_param_tte(
      dataset_adsl = adsl,
      source_datasets = list(adsl = adsl, adrs = adrs),
      start_date = TRTSDT,
      event_conditions = list(pd, death),
      censor_conditions = list(lastvisit, start),
      set_values_to = exprs(PARAMCD = "PFS", PARAM = "Progression Free Survival")
    )

USUBJID | PARAMCD | STARTDT | ADT | ADTF | CNSR
--- | --- | --- | --- | --- | ---
01 | PFS | 2021-01-01 | 2021-05-05 | NA | 0
02 | PFS | 2021-02-03 | 2021-02-03 | NA | 0
03 | PFS | 2021-08-10 | 2021-08-21 | NA | 0
04 | PFS | 2021-02-03 | 2021-05-15 | NA | 1
05 | PFS | 2021-04-01 | 2021-04-01 | D | 2

### Deriving a Series of Time-to-Event Parameters

If several similar time-to-event parameters need to be derived the `call_derivation()` function is useful.

In the following example parameters for time to first AE, time to first serious AE, and time to first related AE are derived. The censoring is the same for all three. Only the definition of the event differs.

    # define censoring #
    observation_end <- censor_source(
      dataset_name = "adsl",
      date = pmin(TRTEDT + days(30), EOSDT),
      censor = 1,
      set_values_to = exprs(
        EVNTDESC = "END OF TREATMENT",
        SRCDOM = "ADSL",
        SRCVAR = "TRTEDT"
      )
    )

    # define time to first AE #
    tt_ae <- event_source(
      dataset_name = "ae",
      date = ASTDT,
      order = exprs(AESEQ),
      set_values_to = exprs(
        EVNTDESC = "ADVERSE EVENT",
        SRCDOM = "AE",
        SRCVAR = "AESTDTC",
        SRCSEQ = AESEQ
      )
    )

    # define time to first serious AE #
    tt_ser_ae <- event_source(
      dataset_name = "ae",
      filter = AESER == "Y",
      date = ASTDT,
      order = exprs(AESEQ),
      set_values_to = exprs(
        EVNTDESC = "SERIOUS ADVERSE EVENT",
        SRCDOM = "AE",
        SRCVAR = "AESTDTC",
        SRCSEQ = AESEQ
      )
    )

    # define time to first related AE #
    tt_rel_ae <- event_source(
      dataset_name = "ae",
      filter = AEREL %in% c("PROBABLE", "POSSIBLE", "REMOTE"),
      date = ASTDT,
      order = exprs(AESEQ),
      set_values_to = exprs(
        EVNTDESC = "RELATED ADVERSE EVENT",
        SRCDOM = "AE",
        SRCVAR = "AESTDTC",
        SRCSEQ = AESEQ
      )
    )

    # derive all three time to event parameters #
    adaette <- call_derivation(
      derivation = derive_param_tte,
      variable_params = list(
        params(
          event_conditions = list(tt_ae),
          set_values_to = exprs(PARAMCD = "TTAE")
        ),
        params(
          event_conditions = list(tt_ser_ae),
          set_values_to = exprs(PARAMCD = "TTSERAE")
        ),
        params(
          event_conditions = list(tt_rel_ae),
          set_values_to = exprs(PARAMCD = "TTRELAE")
        )
      ),
      dataset_adsl = adsl,
      source_datasets = list(
        adsl = adsl,
        ae = filter(adae, TRTEMFL == "Y")
      ),
      censor_conditions = list(observation_end)
    )

USUBJID | PARAMCD | STARTDT | ADT | CNSR | EVNTDESC | SRCDOM | SRCVAR | SRCSEQ
--- | --- | --- | --- | --- | --- | --- | --- | ---
01-701-1015 | TTAE | 2014-01-02 | 2014-01-09 | 0 | ADVERSE EVENT | AE | AESTDTC | 3
01-701-1015 | TTRELAE | 2014-01-02 | 2014-01-09 | 0 | RELATED ADVERSE EVENT | AE | AESTDTC | 3
01-701-1015 | TTSERAE | 2014-01-02 | 2014-07-02 | 1 | END OF TREATMENT | ADSL | TRTEDT | NA
01-701-1023 | TTAE | 2012-08-05 | 2012-08-07 | 0 | ADVERSE EVENT | AE | AESTDTC | 1
01-701-1023 | TTRELAE | 2012-08-05 | 2012-08-07 | 0 | RELATED ADVERSE EVENT | AE | AESTDTC | 1
01-701-1023 | TTSERAE | 2012-08-05 | 2012-09-02 | 1 | END OF TREATMENT | ADSL | TRTEDT | NA
01-701-1028 | TTAE | 2013-07-19 | 2014-01-14 | 1 | END OF TREATMENT | ADSL | TRTEDT | NA
01-701-1028 | TTRELAE | 2013-07-19 | 2014-01-14 | 1 | END OF TREATMENT | ADSL | TRTEDT | NA
01-701-1028 | TTSERAE | 2013-07-19 | 2014-01-14 | 1 | END OF TREATMENT | ADSL | TRTEDT | NA
01-701-1033 | TTAE | 2014-03-18 | 2014-04-14 | 1 | END OF TREATMENT | ADSL | TRTEDT | NA

If time-to-event parameters need to be derived for each by group of a source dataset, the `by_vars` parameter can be specified. See the `derive_param_tte()` documentation for an example.

### Further Scenarios

For further scenarios on how to derive data for time-to-event analyses, see the Time-to-Event Analyses vignette.

## Derive Analysis Value (`AVAL`)

The analysis value (`AVAL`) can be derived by calling `derive_vars_duration()`.

This example derives the time to event in days. Other units can be requested by the specifying the `out_unit` parameter.

    adtte <- derive_vars_duration(
      adtte,
      new_var = AVAL,
      start_date = STARTDT,
      end_date = ADT
    )

STUDYID | USUBJID | EVNTDESC | SRCDOM | SRCVAR | CNSR | ADT | STARTDT | PARAMCD | PARAM | AVAL
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
CDISCPILOT01 | 01-701-1015 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-07-02 | 2014-01-02 | OS | Overall Survival | 182
CDISCPILOT01 | 01-701-1023 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2012-09-02 | 2012-08-05 | OS | Overall Survival | 29
CDISCPILOT01 | 01-701-1028 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-01-14 | 2013-07-19 | OS | Overall Survival | 180
CDISCPILOT01 | 01-701-1033 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-04-14 | 2014-03-18 | OS | Overall Survival | 28
CDISCPILOT01 | 01-701-1034 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-12-30 | 2014-07-01 | OS | Overall Survival | 183
CDISCPILOT01 | 01-701-1047 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2013-04-07 | 2013-02-12 | OS | Overall Survival | 55
CDISCPILOT01 | 01-701-1097 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-07-09 | 2014-01-01 | OS | Overall Survival | 190
CDISCPILOT01 | 01-701-1111 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2012-09-17 | 2012-09-07 | OS | Overall Survival | 11
CDISCPILOT01 | 01-701-1115 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2013-01-23 | 2012-11-30 | OS | Overall Survival | 55
CDISCPILOT01 | 01-701-1118 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-09-09 | 2014-03-12 | OS | Overall Survival | 182

## Derive Analysis Sequence Number (`ASEQ`)

The `{admiral}` function `derive_var_obs_number()` can be used to derive `ASEQ`. Note that creating `ASEQ` is not required for all ADaM datasets according to the ADaM IG, and this is just for demonstration purpose.

    # Calculate ASEQ (Optional Variable)
    adtte <- derive_var_obs_number(
      adtte,
      by_vars = exprs(STUDYID, USUBJID),
      order = exprs(PARAMCD),
      check_type = "error"
    )

STUDYID | USUBJID | EVNTDESC | SRCDOM | SRCVAR | CNSR | ADT | STARTDT | PARAMCD | PARAM | AVAL | ASEQ
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
CDISCPILOT01 | 01-701-1015 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-07-02 | 2014-01-02 | OS | Overall Survival | 182 | 1
CDISCPILOT01 | 01-701-1023 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2012-09-02 | 2012-08-05 | OS | Overall Survival | 29 | 1
CDISCPILOT01 | 01-701-1028 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-01-14 | 2013-07-19 | OS | Overall Survival | 180 | 1
CDISCPILOT01 | 01-701-1033 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-04-14 | 2014-03-18 | OS | Overall Survival | 28 | 1
CDISCPILOT01 | 01-701-1034 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-12-30 | 2014-07-01 | OS | Overall Survival | 183 | 1
CDISCPILOT01 | 01-701-1047 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2013-04-07 | 2013-02-12 | OS | Overall Survival | 55 | 1
CDISCPILOT01 | 01-701-1097 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-07-09 | 2014-01-01 | OS | Overall Survival | 190 | 1
CDISCPILOT01 | 01-701-1111 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2012-09-17 | 2012-09-07 | OS | Overall Survival | 11 | 1
CDISCPILOT01 | 01-701-1115 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2013-01-23 | 2012-11-30 | OS | Overall Survival | 55 | 1
CDISCPILOT01 | 01-701-1118 | LAST KNOWN ALIVE DATE | ADSL | LSTALVDT | 1 | 2014-09-09 | 2014-03-12 | OS | Overall Survival | 182 | 1

## Add ADSL Variables

Variables from ADSL which are required for time-to-event analyses, e.g., treatment variables or covariates can be added using `derive_vars_merged()`.

    adtte <- derive_vars_merged(
      adtte,
      dataset_add = adsl,
      new_vars = exprs(ARMCD, ARM, ACTARMCD, ACTARM, AGE, SEX),
      by_vars = exprs(STUDYID, USUBJID)
    )

USUBJID | PARAMCD | CNSR | AVAL | ARMCD | AGE | SEX
--- | --- | --- | --- | --- | --- | ---
01-701-1015 | OS | 1 | 182 | Pbo | 63 | F
01-701-1023 | OS | 1 | 29 | Pbo | 64 | M
01-701-1028 | OS | 1 | 180 | Xan_Hi | 71 | M
01-701-1033 | OS | 1 | 28 | Xan_Lo | 74 | M
01-701-1034 | OS | 1 | 183 | Xan_Hi | 77 | F
01-701-1047 | OS | 1 | 55 | Pbo | 85 | F
01-701-1097 | OS | 1 | 190 | Xan_Lo | 68 | M
01-701-1111 | OS | 1 | 11 | Xan_Lo | 81 | F
01-701-1115 | OS | 1 | 55 | Xan_Lo | 84 | M
01-701-1118 | OS | 1 | 182 | Pbo | 52 | M

## Add Labels and Attributes

Note that attributes may not be preserved in some cases after processing with `{admiral}`. The recommended approach is to apply variable labels and other metadata as a final step in your data derivation process using packages like:

- 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: Together with `{admiral}` these packages comprise an End to End pipeline under the umbrella of the pharmaverse. An example of applying metadata and performing associated checks can be found at the pharmaverse E2E example.