# Creating a PK NCA or Population PK ADaM

# Introduction

This article describes creating a Pharmacokinetics (PK) Non-compartmental analysis (NCA) ADaM (ADNCA/ADPC) or a Population PK ADaM (ADPPK). The first part of the article describes the NCA file creation while the second part describes Population PK. This initial steps for both files are very similar and could be combined in one script if desired.

# Programming PK NCA (ADPC/ADNCA) Analysis Data

The Non-compartmental analysis (NCA) ADaM uses the CDISC Implementation Guide (https://www.cdisc.org/standards/foundational/adam/adamig-non-compartmental-analysis-input-data-v1-0). This example presented uses underlying `EX` and `PC` domains where the `EX` and `PC` domains represent data as collected and the `ADPC` ADaM is output. However, the example can be applied to situations where an `EC` domain is used as input instead of `EX` and/or `ADNCA` or another ADaM is created.

One of the important aspects of the dataset is the derivation of relative timing variables. These variables consist of nominal and actual times, and refer to the time from first dose or time from most recent reference dose. The reference dose for pre-dose records may be the upcoming dose. The CDISC Implementation Guide makes use of duplicated records for analysis, which allows the same record to be used both with respect to the previous dose and the next upcoming dose. This is illustrated later in this vignette.

Here are the relative time variables we will use. These correspond to the names in the CDISC Implementation Guide.

Variable | Variable Label
--- | ---
NFRLT | Nom. Rel. Time from Analyte First Dose
AFRLT | Act. Rel. Time from Analyte First Dose
NRRLT | Nominal Rel. Time from Ref. Dose
ARRLT | Actual Rel. Time from Ref. Dose
MRRLT | Modified Rel. Time from Ref. Dose

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

# `ADPC` Programming Workflow

- Read in Data

- Expand Dosing Records

- Find First Dose

- Find Reference Dose Dates Corresponding to PK Records

- Combine `PC` and `EX` Records and Derive Relative Time Variables

- Derive Analysis Variables

- Create `DTYPE` for Imputed records

- Create Duplicated Records for Analysis

- Combine `ADPC` data with Duplicated Records

- Calculate Change from Baseline and Assign `ASEQ`

- Add Additional Baseline Variables

- Add ADSL variables

- Add Labels and Attributes

## Read in Data

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

Additional domains such as `VS` and `LB` may be used for additional baseline variables if needed. These may come from either the SDTM or ADaM source.

For the purpose of example, the CDISC Pilot SDTM and ADaM datasets—which are included in `{pharmaversesdtm}`—are used.

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

    ex <- pharmaversesdtm::ex
    pc <- pharmaversesdtm::pc
    vs <- pharmaversesdtm::vs
    lb <- pharmaversesdtm::lb
    adsl <- admiral::admiral_adsl

    ex <- convert_blanks_to_na(ex)
    pc <- convert_blanks_to_na(pc)
    vs <- convert_blanks_to_na(vs)
    lb <- convert_blanks_to_na(lb) %>%
      filter(LBBLFL == "Y")

    # ---- Lookup tables ----
    param_lookup <- tibble::tribble(
      ~PCTESTCD, ~PARAMCD, ~PARAM, ~PARAMN,
      "XAN", "XAN", "Pharmacokinetic concentration of Xanomeline", 1,
      "DOSE", "DOSE", "Xanomeline Patch Dose", 2,
    )

At this step, it may be useful to join `ADSL` to your `PC` and `EX` domains as well. Only the `ADSL` variables used for derivations are selected at this step. The rest of the relevant `ADSL` variables will be added later.

In this case we will keep `TRTSDT`/`TRTSDTM` for day derivation and `TRT01P`/`TRT01A` for planned and actual treatments.

In this segment we will use `derive_vars_merged()` to join the `ADSL` variables and the following `{admiral}` functions to derive analysis dates, times and days: `derive_vars_dtm()`, `derive_vars_dtm_to_dt()`, `derive_vars_dtm_to_tm()`, `derive_vars_dy()`. We will also create `NFRLT` for `PC` data based on `PCTPTNUM`. We will create an event ID (`EVID`) of 0 for concentration records and 1 for dosing records. This is a traditional variable that will provide a handy tool to identify records but will be dropped from the final dataset in this example.

    adsl_vars <- exprs(TRTSDT, TRTSDTM, TRT01P, TRT01A)

    pc_dates <- pc %>%
      # Join ADSL with PC (need TRTSDT for ADY derivation)
      derive_vars_merged(
        dataset_add = adsl,
        new_vars = adsl_vars,
        by_vars = exprs(STUDYID, USUBJID)
      ) %>%
      # Derive analysis date/time
      # Impute missing time to 00:00:00
      derive_vars_dtm(
        new_vars_prefix = "A",
        dtc = PCDTC,
        time_imputation = "00:00:00"
      ) %>%
      # Derive dates and times from date/times
      derive_vars_dtm_to_dt(exprs(ADTM)) %>%
      derive_vars_dtm_to_tm(exprs(ADTM)) %>%
      derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT)) %>%
      # Derive event ID and nominal relative time from first dose (NFRLT)
      mutate(
        EVID = 0,
        DRUG = PCTEST,
        NFRLT = if_else(PCTPTNUM < 0, 0, PCTPTNUM), .after = USUBJID
      )

USUBJID | PCTEST | ADTM | VISIT | PCTPT | NFRLT
--- | --- | --- | --- | --- | ---
01-701-1028 | XANOMELINE | 2013-07-18 23:30:00 | BASELINE | Pre-dose | 0.00
01-701-1028 | XANOMELINE | 2013-07-19 00:05:00 | BASELINE | 5 Min Post-dose | 0.08
01-701-1028 | XANOMELINE | 2013-07-19 00:30:00 | BASELINE | 30 Min Post-dose | 0.50
01-701-1028 | XANOMELINE | 2013-07-19 01:00:00 | BASELINE | 1h Post-dose | 1.00
01-701-1028 | XANOMELINE | 2013-07-19 01:30:00 | BASELINE | 1.5h Post-dose | 1.50
01-701-1028 | XANOMELINE | 2013-07-19 02:00:00 | BASELINE | 2h Post-dose | 2.00
01-701-1028 | XANOMELINE | 2013-07-19 04:00:00 | BASELINE | 4h Post-dose | 4.00
01-701-1028 | XANOMELINE | 2013-07-19 06:00:00 | BASELINE | 6h Post-dose | 6.00
01-701-1028 | XANOMELINE | 2013-07-19 08:00:00 | BASELINE | 8h Post-dose | 8.00
01-701-1028 | XANOMELINE | 2013-07-19 12:00:00 | BASELINE | 12h Post-dose | 12.00

Next we will also join `ADSL` data with `EX` and derive dates/times. This section uses the `{admiral}` functions `derive_vars_merged()`, `derive_vars_dtm()`, and `derive_vars_dtm_to_dt()`. Time is imputed to 00:00:00 here for reasons specific to the sample data. Other imputation times may be used based on study details. Here we create `NFRLT` for `EX` data based on `VISITDY` using `dplyr::mutate()`.

    # ---- Get dosing information ----

    ex_dates <- ex %>%
      derive_vars_merged(
        dataset_add = adsl,
        new_vars = adsl_vars,
        by_vars = exprs(STUDYID, USUBJID)
      ) %>%
      # Keep records with nonzero dose
      filter(EXDOSE > 0) %>%
      # Add time and set missing end date to start date
      # Impute missing time to 00:00:00
      # Note all times are missing for dosing records in this example data
      # Derive Analysis Start and End Dates
      derive_vars_dtm(
        new_vars_prefix = "AST",
        dtc = EXSTDTC,
        time_imputation = "00:00:00"
      ) %>%
      derive_vars_dtm(
        new_vars_prefix = "AEN",
        dtc = EXENDTC,
        time_imputation = "00:00:00"
      ) %>%
      # Derive event ID and nominal relative time from first dose (NFRLT)
      mutate(
        EVID = 1,
        NFRLT = case_when(
          VISITDY == 1 ~ 0,
          TRUE ~ 24 * VISITDY
        )
      ) %>%
      # Set missing end dates to start date
      mutate(AENDTM = case_when(
        is.na(AENDTM) ~ ASTDTM,
        TRUE ~ AENDTM
      )) %>%
      # Derive dates from date/times
      derive_vars_dtm_to_dt(exprs(ASTDTM)) %>%
      derive_vars_dtm_to_dt(exprs(AENDTM))

USUBJID | EXTRT | EXDOSFRQ | ASTDTM | AENDTM | VISIT | VISITDY | NFRLT
--- | --- | --- | --- | --- | --- | --- | ---
01-701-1028 | XANOMELINE | QD | 2013-07-19 | 2013-08-01 | BASELINE | 1 | 0
01-701-1028 | XANOMELINE | QD | 2013-08-02 | 2014-01-06 | WEEK 2 | 14 | 336
01-701-1028 | XANOMELINE | QD | 2014-01-07 | 2014-01-14 | WEEK 24 | 168 | 4032
01-701-1033 | XANOMELINE | QD | 2014-03-18 | 2014-03-31 | BASELINE | 1 | 0
01-701-1442 | XANOMELINE | QD | 2013-10-26 | 2013-11-09 | BASELINE | 1 | 0
01-701-1442 | XANOMELINE | QD | 2013-11-10 | 2014-04-17 | WEEK 2 | 14 | 336
01-701-1442 | XANOMELINE | QD | 2014-04-18 | 2014-04-26 | WEEK 24 | 168 | 4032
01-714-1288 | XANOMELINE | QD | 2013-12-04 | 2013-12-17 | BASELINE | 1 | 0
01-714-1288 | XANOMELINE | QD | 2013-12-18 | 2014-05-27 | WEEK 2 | 14 | 336
01-714-1288 | XANOMELINE | QD | 2014-05-28 | 2014-06-17 | WEEK 24 | 168 | 4032

## Expand Dosing Records

The function `create_single_dose_dataset()` can be used to expand dosing records between the start date and end date. The nominal time will also be expanded based on the values of `EXDOSFRQ`, for example “QD” will result in nominal time being incremented by 24 hours and “BID” will result in nominal time being incremented by 12 hours. This is a new feature of `create_single_dose_dataset()`.

Dates and times will be derived after expansion using `derive_vars_dtm_to_dt()` and `derive_vars_dtm_to_tm()`.

For this example study we will define analysis visit (`AVISIT)` based on the nominal day value from `NFRLT` and give it the format, “Day 1”, “Day 2”, “Day 3”, etc. This is important for creating the `BASETYPE` variable later. `DRUG` is created from `EXTRT` here. This will be useful for linking treatment data with concentration data if there are multiple drugs and/or analytes, but this variable will also be dropped from the final dataset in this example.

    # ---- Expand dosing records between start and end dates ----

    ex_exp <- ex_dates %>%
      create_single_dose_dataset(
        dose_freq = EXDOSFRQ,
        start_date = ASTDT,
        start_datetime = ASTDTM,
        end_date = AENDT,
        end_datetime = AENDTM,
        nominal_time = NFRLT,
        lookup_table = dose_freq_lookup,
        lookup_column = CDISC_VALUE,
        keep_source_vars = exprs(
          STUDYID, USUBJID, EVID, EXDOSFRQ, EXDOSFRM,
          NFRLT, EXDOSE, EXDOSU, EXTRT, ASTDT, ASTDTM, AENDT, AENDTM,
          VISIT, VISITNUM, VISITDY, TRT01A, TRT01P, DOMAIN, EXSEQ, !!!adsl_vars
        )
      ) %>%
      # Derive AVISIT based on nominal relative time
      # Derive AVISITN to nominal time in whole days using integer division
      # Define AVISIT based on nominal day
      mutate(
        AVISITN = NFRLT %/% 24 + 1,
        AVISIT = paste("Day", AVISITN),
        ADTM = ASTDTM,
        DRUG = EXTRT,
      ) %>%
      # Derive dates and times from datetimes
      derive_vars_dtm_to_dt(exprs(ADTM)) %>%
      derive_vars_dtm_to_tm(exprs(ADTM)) %>%
      derive_vars_dtm_to_tm(exprs(ASTDTM)) %>%
      derive_vars_dtm_to_tm(exprs(AENDTM)) %>%
      derive_vars_dy(reference_date = TRTSDT, source_vars = exprs(ADT))

USUBJID | DRUG | EXDOSFRQ | ASTDTM | AENDTM | AVISIT | NFRLT
--- | --- | --- | --- | --- | --- | ---
01-701-1028 | XANOMELINE | ONCE | 2013-07-19 | 2013-07-19 | Day 1 | 0
01-701-1028 | XANOMELINE | ONCE | 2013-07-20 | 2013-07-20 | Day 2 | 24
01-701-1028 | XANOMELINE | ONCE | 2013-07-21 | 2013-07-21 | Day 3 | 48
01-701-1028 | XANOMELINE | ONCE | 2013-07-22 | 2013-07-22 | Day 4 | 72
01-701-1028 | XANOMELINE | ONCE | 2013-07-23 | 2013-07-23 | Day 5 | 96
01-701-1028 | XANOMELINE | ONCE | 2013-07-24 | 2013-07-24 | Day 6 | 120
01-701-1028 | XANOMELINE | ONCE | 2013-07-25 | 2013-07-25 | Day 7 | 144
01-701-1028 | XANOMELINE | ONCE | 2013-07-26 | 2013-07-26 | Day 8 | 168
01-701-1028 | XANOMELINE | ONCE | 2013-07-27 | 2013-07-27 | Day 9 | 192
01-701-1028 | XANOMELINE | ONCE | 2013-07-28 | 2013-07-28 | Day 10 | 216

## Find First Dose

In this section we will find the first dose for each subject and drug, using `derive_vars_merged()`. We also create an analysis visit (`AVISIT`) based on `NFRLT`. The first dose datetime for an analyte `FANLDTM` is calculated as the minimum `ADTM` from the dosing records by subject and drug.

    # ---- Find first dose per treatment per subject ----
    # ---- Join with ADPC data and keep only subjects with dosing ----

    adpc_first_dose <- pc_dates %>%
      derive_vars_merged(
        dataset_add = ex_exp,
        filter_add = (EXDOSE > 0 & !is.na(ADTM)),
        new_vars = exprs(FANLDTM = ADTM),
        order = exprs(ADTM, EXSEQ),
        mode = "first",
        by_vars = exprs(STUDYID, USUBJID, DRUG)
      ) %>%
      filter(!is.na(FANLDTM)) %>%
      # Derive AVISIT based on nominal relative time
      # Derive AVISITN to nominal time in whole days using integer division
      # Define AVISIT based on nominal day
      mutate(
        AVISITN = NFRLT %/% 24 + 1,
        AVISIT = paste("Day", AVISITN)
      )

USUBJID | FANLDTM | AVISIT | ADTM | PCTPT
--- | --- | --- | --- | ---
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-18 23:30:00 | Pre-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 00:05:00 | 5 Min Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 00:30:00 | 30 Min Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 01:00:00 | 1h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 01:30:00 | 1.5h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 02:00:00 | 2h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 04:00:00 | 4h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 06:00:00 | 6h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 08:00:00 | 8h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 12:00:00 | 12h Post-dose

## Find Reference Dose Dates Corresponding to PK Records

Use `derive_vars_joined()` to find the previous dose data. This will join the expanded `EX` data with the `ADPC` based on the analysis date `ADTM`. Note the `filter_join` parameter. In addition to the date of the previous dose (`ADTM_prev)`, we also keep the actual dose amount `EXDOSE_prev` and the analysis visit of the dose `AVISIT_prev`.

    # ---- Find previous dose  ----

    adpc_prev <- adpc_first_dose %>%
      derive_vars_joined(
        dataset_add = ex_exp,
        by_vars = exprs(USUBJID),
        order = exprs(ADTM),
        new_vars = exprs(
          ADTM_prev = ADTM, EXDOSE_prev = EXDOSE, AVISIT_prev = AVISIT,
          AENDTM_prev = AENDTM
        ),
        join_vars = exprs(ADTM),
        join_type = "all",
        filter_add = NULL,
        filter_join = ADTM > ADTM.join,
        mode = "last",
        check_type = "none"
      )

USUBJID | VISIT | ADTM | PCTPT | ADTM_prev | EXDOSE_prev | AVISIT_prev
--- | --- | --- | --- | --- | --- | ---
01-701-1028 | BASELINE | 2013-07-18 23:30:00 | Pre-dose | NA | NA | NA
01-701-1028 | BASELINE | 2013-07-19 00:05:00 | 5 Min Post-dose | 2013-07-19 | 54 | Day 1
01-701-1028 | BASELINE | 2013-07-19 00:30:00 | 30 Min Post-dose | 2013-07-19 | 54 | Day 1
01-701-1028 | BASELINE | 2013-07-19 01:00:00 | 1h Post-dose | 2013-07-19 | 54 | Day 1
01-701-1028 | BASELINE | 2013-07-19 01:30:00 | 1.5h Post-dose | 2013-07-19 | 54 | Day 1
01-701-1028 | BASELINE | 2013-07-19 02:00:00 | 2h Post-dose | 2013-07-19 | 54 | Day 1
01-701-1028 | BASELINE | 2013-07-19 04:00:00 | 4h Post-dose | 2013-07-19 | 54 | Day 1
01-701-1028 | BASELINE | 2013-07-19 06:00:00 | 6h Post-dose | 2013-07-19 | 54 | Day 1
01-701-1028 | BASELINE | 2013-07-19 08:00:00 | 8h Post-dose | 2013-07-19 | 54 | Day 1
01-701-1028 | BASELINE | 2013-07-19 12:00:00 | 12h Post-dose | 2013-07-19 | 54 | Day 1

Similarly, find next dose information using `derive_vars_joined()` with the `filter_join` parameter as `ADTM <= ADTM.join`. Here we keep the next dose analysis date `ADTM_next`, the next actual dose `EXDOSE_next`, and the next analysis visit `AVISIT_next`.

    # ---- Find next dose  ----

    adpc_next <- adpc_prev %>%
      derive_vars_joined(
        dataset_add = ex_exp,
        by_vars = exprs(USUBJID),
        order = exprs(ADTM),
        new_vars = exprs(
          ADTM_next = ADTM, EXDOSE_next = EXDOSE, AVISIT_next = AVISIT,
          AENDTM_next = AENDTM
        ),
        join_vars = exprs(ADTM),
        join_type = "all",
        filter_add = NULL,
        filter_join = ADTM <= ADTM.join,
        mode = "first",
        check_type = "none"
      )

USUBJID | VISIT | ADTM | PCTPT | ADTM_next | EXDOSE_next | AVISIT_next
--- | --- | --- | --- | --- | --- | ---
01-701-1028 | BASELINE | 2013-07-18 23:30:00 | Pre-dose | 2013-07-19 | 54 | Day 1
01-701-1028 | BASELINE | 2013-07-19 00:05:00 | 5 Min Post-dose | 2013-07-20 | 54 | Day 2
01-701-1028 | BASELINE | 2013-07-19 00:30:00 | 30 Min Post-dose | 2013-07-20 | 54 | Day 2
01-701-1028 | BASELINE | 2013-07-19 01:00:00 | 1h Post-dose | 2013-07-20 | 54 | Day 2
01-701-1028 | BASELINE | 2013-07-19 01:30:00 | 1.5h Post-dose | 2013-07-20 | 54 | Day 2
01-701-1028 | BASELINE | 2013-07-19 02:00:00 | 2h Post-dose | 2013-07-20 | 54 | Day 2
01-701-1028 | BASELINE | 2013-07-19 04:00:00 | 4h Post-dose | 2013-07-20 | 54 | Day 2
01-701-1028 | BASELINE | 2013-07-19 06:00:00 | 6h Post-dose | 2013-07-20 | 54 | Day 2
01-701-1028 | BASELINE | 2013-07-19 08:00:00 | 8h Post-dose | 2013-07-20 | 54 | Day 2
01-701-1028 | BASELINE | 2013-07-19 12:00:00 | 12h Post-dose | 2013-07-20 | 54 | Day 2

Use the same method to find the previous and next nominal times. Note that here the data are sorted by nominal time rather than the actual time. This will tell us when the previous dose and the next dose were supposed to occur. Sometimes this will differ from the actual times in a study. Here we keep the previous nominal dose time `NFRLT_prev` and the next nominal dose time `NFRLT_next`. Note that the `filter_join` parameter uses the nominal relative times, e.g. `NFRLT > NFRLT.join`.

    # ---- Find previous nominal time ----

    adpc_nom_prev <- adpc_next %>%
      derive_vars_joined(
        dataset_add = ex_exp,
        by_vars = exprs(USUBJID),
        order = exprs(NFRLT),
        new_vars = exprs(NFRLT_prev = NFRLT),
        join_vars = exprs(NFRLT),
        join_type = "all",
        filter_add = NULL,
        filter_join = NFRLT > NFRLT.join,
        mode = "last",
        check_type = "none"
      )

    # ---- Find next nominal time ----

    adpc_nom_next <- adpc_nom_prev %>%
      derive_vars_joined(
        dataset_add = ex_exp,
        by_vars = exprs(USUBJID),
        order = exprs(NFRLT),
        new_vars = exprs(NFRLT_next = NFRLT),
        join_vars = exprs(NFRLT),
        join_type = "all",
        filter_add = NULL,
        filter_join = NFRLT <= NFRLT.join,
        mode = "first",
        check_type = "none"
      )

USUBJID | NFRLT | PCTPT | NFRLT_prev | NFRLT_next
--- | --- | --- | --- | ---
01-701-1028 | 0.00 | Pre-dose | NA | 0
01-701-1028 | 0.08 | 5 Min Post-dose | 0 | 24
01-701-1028 | 0.50 | 30 Min Post-dose | 0 | 24
01-701-1028 | 1.00 | 1h Post-dose | 0 | 24
01-701-1028 | 1.50 | 1.5h Post-dose | 0 | 24
01-701-1028 | 2.00 | 2h Post-dose | 0 | 24
01-701-1028 | 4.00 | 4h Post-dose | 0 | 24
01-701-1028 | 6.00 | 6h Post-dose | 0 | 24
01-701-1028 | 8.00 | 8h Post-dose | 0 | 24
01-701-1028 | 12.00 | 12h Post-dose | 0 | 24

## Combine PC and EX Records and Derive Relative Time Variables

Combine `PC` and `EX` records and derive the additional relative time variables. Often NCA data will keep both dosing and concentration records. We will keep both here. Sometimes you will see `ADPC` with only the concentration records. If this is desired, the dosing records can be dropped before saving the final dataset. We will use the `{admiral}` function `derive_vars_duration()` to calculate the actual relative time from first dose (`AFRLT`) and the actual relative time from most recent dose (`ARRLT`). Note that we use the parameter `add_one = FALSE` here. We will also create a variable representing actual time to next dose (`AXRLT`) which is not kept, but will be used when we create duplicated records for analysis for the pre-dose records. For now, we will update missing values of `ARRLT` corresponding to the pre-dose records with `AXRLT`, and dosing records will be set to zero.

We also calculate the reference dates `FANLDTM` (First Datetime of Dose for Analyte) and `PCRFTDTM` (Reference Datetime of Dose for Analyte) and their corresponding date and time variables.

We calculate the maximum date for concentration records and only keep the dosing records up to that date.

    # ---- Combine ADPC and EX data ----
    # Derive Relative Time Variables

    adpc_arrlt <- bind_rows(adpc_nom_next, ex_exp) %>%
      group_by(USUBJID, DRUG) %>%
      mutate(
        FANLDTM = min(FANLDTM, na.rm = TRUE),
        min_NFRLT = min(NFRLT_prev, na.rm = TRUE),
        maxdate = max(ADT[EVID == 0], na.rm = TRUE), .after = USUBJID
      ) %>%
      arrange(USUBJID, ADTM) %>%
      ungroup() %>%
      filter(ADT <= maxdate) %>%
      # Derive Actual Relative Time from First Dose (AFRLT)
      derive_vars_duration(
        new_var = AFRLT,
        start_date = FANLDTM,
        end_date = ADTM,
        out_unit = "hours",
        floor_in = FALSE,
        add_one = FALSE
      ) %>%
      # Derive Actual Relative Time from Reference Dose (ARRLT)
      derive_vars_duration(
        new_var = ARRLT,
        start_date = ADTM_prev,
        end_date = ADTM,
        out_unit = "hours",
        floor_in = FALSE,
        add_one = FALSE
      ) %>%
      # Derive Actual Relative Time from Next Dose (AXRLT not kept)
      derive_vars_duration(
        new_var = AXRLT,
        start_date = ADTM_next,
        end_date = ADTM,
        out_unit = "hours",
        floor_in = FALSE,
        add_one = FALSE
      ) %>%
      mutate(
        ARRLT = case_when(
          EVID == 1 ~ 0,
          is.na(ARRLT) ~ AXRLT,
          TRUE ~ ARRLT
        ),

        # Derive Reference Dose Date
        PCRFTDTM = case_when(
          EVID == 1 ~ ADTM,
          is.na(ADTM_prev) ~ ADTM_next,
          TRUE ~ ADTM_prev
        )
      ) %>%
      # Derive dates and times from datetimes
      derive_vars_dtm_to_dt(exprs(FANLDTM)) %>%
      derive_vars_dtm_to_tm(exprs(FANLDTM)) %>%
      derive_vars_dtm_to_dt(exprs(PCRFTDTM)) %>%
      derive_vars_dtm_to_tm(exprs(PCRFTDTM))

USUBJID | FANLDTM | AVISIT | PCTPT | AFRLT | ARRLT | AXRLT
--- | --- | --- | --- | --- | --- | ---
01-701-1028 | 2013-07-19 | Day 1 | Pre-dose | -0.5000000 | -0.5000000 | -0.50000
01-701-1028 | 2013-07-19 | Day 1 | NA | 0.0000000 | 0.0000000 | NA
01-701-1028 | 2013-07-19 | Day 1 | 5 Min Post-dose | 0.0833333 | 0.0833333 | -23.91667
01-701-1028 | 2013-07-19 | Day 1 | 30 Min Post-dose | 0.5000000 | 0.5000000 | -23.50000
01-701-1028 | 2013-07-19 | Day 1 | 1h Post-dose | 1.0000000 | 1.0000000 | -23.00000
01-701-1028 | 2013-07-19 | Day 1 | 1.5h Post-dose | 1.5000000 | 1.5000000 | -22.50000
01-701-1028 | 2013-07-19 | Day 1 | 2h Post-dose | 2.0000000 | 2.0000000 | -22.00000
01-701-1028 | 2013-07-19 | Day 1 | 4h Post-dose | 4.0000000 | 4.0000000 | -20.00000
01-701-1028 | 2013-07-19 | Day 1 | 6h Post-dose | 6.0000000 | 6.0000000 | -18.00000
01-701-1028 | 2013-07-19 | Day 1 | 0-6h Post-dose | 6.0000000 | 6.0000000 | -18.00000

For nominal relative times we calculate `NRRLT` generally as `NFRLT - NFRLT_prev` and `NXRLT` as `NFRLT - NFRLT_next`.

    adpc_nrrlt <- adpc_arrlt %>%
      # Derive Nominal Relative Time from Reference Dose (NRRLT)
      mutate(
        NRRLT = case_when(
          EVID == 1 ~ 0,
          is.na(NFRLT_prev) ~ NFRLT - min_NFRLT,
          TRUE ~ NFRLT - NFRLT_prev
        ),
        NXRLT = case_when(
          EVID == 1 ~ 0,
          TRUE ~ NFRLT - NFRLT_next
        )
      )

USUBJID | AVISIT | PCTPT | NFRLT | NRRLT | NXRLT
--- | --- | --- | --- | --- | ---
01-701-1028 | Day 1 | Pre-dose | 0.00 | 0.00 | 0.00
01-701-1028 | Day 1 | NA | 0.00 | 0.00 | 0.00
01-701-1028 | Day 1 | 5 Min Post-dose | 0.08 | 0.08 | -23.92
01-701-1028 | Day 1 | 30 Min Post-dose | 0.50 | 0.50 | -23.50
01-701-1028 | Day 1 | 1h Post-dose | 1.00 | 1.00 | -23.00
01-701-1028 | Day 1 | 1.5h Post-dose | 1.50 | 1.50 | -22.50
01-701-1028 | Day 1 | 2h Post-dose | 2.00 | 2.00 | -22.00
01-701-1028 | Day 1 | 4h Post-dose | 4.00 | 4.00 | -20.00
01-701-1028 | Day 1 | 6h Post-dose | 6.00 | 6.00 | -18.00
01-701-1028 | Day 1 | 0-6h Post-dose | 3.00 | 3.00 | -21.00

## Derive Analysis Variables

Using `dplyr::mutate` we derive a number of analysis variables including analysis value (`AVAL`), analysis time point (`ATPT`) analysis timepoint reference (`ATPTREF`) and baseline type (`BASETYPE`).

We set `ATPT` to `PCTPT` for concentration records and to “Dose” for dosing records. The analysis timepoint reference `ATPTREF` will correspond to the dosing visit. We will use `AVISIT_prev` and `AVISIT_next` to derive. The baseline type will be a concatenation of `ATPTREF` and “Baseline” with values such as “Day 1 Baseline”, “Day 2 Baseline”, etc. The baseline flag `ABLFL` will be set to “Y” for pre-dose records.

Analysis value `AVAL` in this example comes from `PCSTRESN` for concentration records. In addition we are including the dose value `EXDOSE` for dosing records.

We derive actual dose `DOSEA` based on `EXDOSE_prev` and `EXDOSE_next` and planned dose `DOSEP` based on the planned treatment `TRT01P`. In addition we add the units for the dose variables and the relative time variables.

    # ---- Derive Analysis Variables ----
    # Derive ATPTN, ATPT, ATPTREF, and BASETYPE
    # Derive planned dose DOSEP, actual dose DOSEA and units
    # Derive PARAMCD and relative time units
    # Derive AVAL, AVALU and AVALCAT1

    adpc_aval <- adpc_nrrlt %>%
      mutate(
        PARCAT1 = PCSPEC,
        ATPTN = case_when(
          EVID == 1 ~ 0,
          TRUE ~ PCTPTNUM
        ),
        ATPT = case_when(
          EVID == 1 ~ "Dose",
          TRUE ~ PCTPT
        ),
        ATPTREF = case_when(
          EVID == 1 ~ AVISIT,
          is.na(AVISIT_prev) ~ AVISIT_next,
          TRUE ~ AVISIT_prev
        ),
        # Derive BASETYPE
        BASETYPE = paste(ATPTREF, "Baseline"),

        # Derive Actual Dose
        DOSEA = case_when(
          EVID == 1 ~ EXDOSE,
          is.na(EXDOSE_prev) ~ EXDOSE_next,
          TRUE ~ EXDOSE_prev
        ),
        # Derive Planned Dose
        DOSEP = case_when(
          TRT01P == "Xanomeline High Dose" ~ 81,
          TRT01P == "Xanomeline Low Dose" ~ 54
        ),
        DOSEU = "mg",
      ) %>%
      # Derive relative time units
      mutate(
        FRLTU = "h",
        RRLTU = "h",
        # Derive PARAMCD
        PARAMCD = coalesce(PCTESTCD, "DOSE"),
        ALLOQ = PCLLOQ,
        # Derive AVAL
        AVAL = case_when(
          EVID == 1 ~ EXDOSE,
          TRUE ~ PCSTRESN
        ),
        AVALU = case_when(
          EVID == 1 ~ EXDOSU,
          TRUE ~ PCSTRESU
        ),
        AVALCAT1 = if_else(PCSTRESC == "<BLQ", PCSTRESC, prettyNum(signif(AVAL, digits = 3))),
      ) %>%
      # Add SRCSEQ
      mutate(
        SRCDOM = DOMAIN,
        SRCVAR = "SEQ",
        SRCSEQ = coalesce(PCSEQ, EXSEQ)
      )

USUBJID | NFRLT | AVISIT | ATPT | ATPTREF | AVAL | AVALCAT1
--- | --- | --- | --- | --- | --- | ---
01-701-1028 | 0.00 | Day 1 | Pre-dose | Day 1 | 0.0000000 | <BLQ
01-701-1028 | 0.00 | Day 1 | Dose | Day 1 | 54.0000000 | NA
01-701-1028 | 0.08 | Day 1 | 5 Min Post-dose | Day 1 | 0.1015662 | 0.102
01-701-1028 | 0.50 | Day 1 | 30 Min Post-dose | Day 1 | 0.5469018 | 0.547
01-701-1028 | 1.00 | Day 1 | 1h Post-dose | Day 1 | 0.9254654 | 0.925
01-701-1028 | 1.50 | Day 1 | 1.5h Post-dose | Day 1 | 1.1875059 | 1.19
01-701-1028 | 2.00 | Day 1 | 2h Post-dose | Day 1 | 1.3688894 | 1.37
01-701-1028 | 4.00 | Day 1 | 4h Post-dose | Day 1 | 1.6831476 | 1.68
01-701-1028 | 6.00 | Day 1 | 6h Post-dose | Day 1 | 1.7552923 | 1.76
01-701-1028 | 3.00 | Day 1 | 0-6h Post-dose | Day 1 | 24.9423721 | 24.9

## Create `DTYPE` for Imputed records

For records that are BLQ (Below Limit of Quantitation), we will create imputed records set to 1/2 of LLOQ. `DTYPE` for these records will be set to “HALFLLOQ”. (Additional tests such as whether more than 1/3 of records are BLQ may be required and are not done in this example.) We also create a listing-ready variable `AVALCAT1` which includes the “BLQ” record indicator and formats the numeric values to three significant digits.

    # ---- Create DTYPE imputation

    adpc_lloq <- adpc_aval %>%
      derive_extreme_records(
        dataset_add = adpc_aval,
        by_vars = exprs(USUBJID, PARAMCD, PARCAT1, AVISITN, AVISIT, ADTM, PCSEQ),
        order = exprs(ADTM, BASETYPE, EVID, ATPTN, PARCAT1),
        mode = "last",
        filter_add = PCSTRESC == "<BLQ" & is.na(AVAL),
        set_values_to = exprs(
          AVAL = ALLOQ * .5,
          DTYPE = "HALFLLOQ"
        )
      )

USUBJID | PARAMCD | PARCAT1 | AVISITN | DTYPE | PCSTRESC | AVAL
--- | --- | --- | --- | --- | --- | ---
01-714-1288 | XAN | PLASMA | 2 | HALFLLOQ | <BLQ | 0.005
01-714-1288 | XAN | PLASMA | 3 | HALFLLOQ | <BLQ | 0.005
01-714-1288 | XAN | URINE | 2 | HALFLLOQ | <BLQ | 0.005
01-718-1101 | XAN | PLASMA | 2 | HALFLLOQ | <BLQ | 0.005
01-718-1101 | XAN | PLASMA | 3 | HALFLLOQ | <BLQ | 0.005
01-718-1101 | XAN | URINE | 2 | HALFLLOQ | <BLQ | 0.005

## Create Duplicated Records for Analysis

As mentioned above, the CDISC ADaM Implementation Guide for Non-compartmental Analysis uses duplicated records for analysis when a record needs to be used in more than one way. In this example the 24 hour post-dose record will also be used a the pre-dose record for the “Day 2” dose. In addition to 24 hour post-dose records, other situations may include pre-dose records for “Cycle 2 Day 1”, etc.

In general, we will select the records of interest and then update the relative time variables for the duplicated records. In this case we will select where the nominal relative time to next dose is zero. (Note that we do not need to duplicate the first dose record since there is no prior dose.)

`DTYPE` is set to “COPY” for the duplicated records and the original `PCSEQ` value is retained. In this case we change “24h Post-dose” to “Pre-dose”. `ABLFL` is set to “Y” since these records will serve as baseline for the “Day 2” dose. `DOSEA` is set to `EXDOSE_next` and `PCRFTDTM` is set to `ADTM_next`.

    # ---- Create DTYPE copy records ----

    dtype <- adpc_lloq %>%
      filter(NFRLT > 0 & NXRLT == 0 & EVID == 0 & !is.na(AVISIT_next)) %>%
      select(-PCRFTDT, -PCRFTTM) %>%
      # Re-derive variables in for DTYPE copy records
      mutate(
        ATPTREF = AVISIT_next,
        ARRLT = AXRLT,
        NRRLT = NXRLT,
        PCRFTDTM = ADTM_next,
        DOSEA = EXDOSE_next,
        BASETYPE = paste(AVISIT_next, "Baseline"),
        ATPT = "Pre-dose",
        ATPTN = -0.5,
        ABLFL = "Y",
        DTYPE = case_when(
          is.na(DTYPE) ~ "COPY",
          DTYPE == "HALFLLOQ" ~ "COPY/HALFLLOQ"
        )
      ) %>%
      derive_vars_dtm_to_dt(exprs(PCRFTDTM)) %>%
      derive_vars_dtm_to_tm(exprs(PCRFTDTM))

USUBJID | DTYPE | ATPT | NFRLT | NRRLT | AFRLT | ARRLT | BASETYPE
--- | --- | --- | --- | --- | --- | --- | ---
01-701-1028 | COPY | Pre-dose | 24 | 0 | 24 | 0 | Day 2 Baseline
01-701-1028 | COPY | Pre-dose | 48 | 0 | 48 | 0 | Day 3 Baseline
01-701-1033 | COPY | Pre-dose | 24 | 0 | 24 | 0 | Day 2 Baseline
01-701-1033 | COPY | Pre-dose | 48 | 0 | 48 | 0 | Day 3 Baseline
01-701-1442 | COPY | Pre-dose | 24 | 0 | 24 | 0 | Day 2 Baseline
01-701-1442 | COPY | Pre-dose | 48 | 0 | 48 | 0 | Day 3 Baseline
01-714-1288 | COPY | Pre-dose | 24 | 0 | 24 | 0 | Day 2 Baseline
01-714-1288 | COPY | Pre-dose | 48 | 0 | 48 | 0 | Day 3 Baseline
01-718-1101 | COPY | Pre-dose | 24 | 0 | 24 | 0 | Day 2 Baseline
01-718-1101 | COPY | Pre-dose | 48 | 0 | 48 | 0 | Day 3 Baseline

## Combine `ADPC` data with Duplicated Records

Now the duplicated records are combined with the original records. We also derive the modified relative time from reference dose `MRRLT`. In this case, negative values of `ARRLT` are set to zero.

This is also an opportunity to derive analysis flags e.g. `ANL01FL` , `ANL02FL` etc. In this example `ANL01FL` is set to “Y” for all records and `ANL02FL` is set to “Y” for all records except the duplicated records with `DTYPE` = “COPY”. Additional flags may be used to select full profile records and/or to select records included in the tables and figures, etc.

    # ---- Combine original records and DTYPE copy records ----

    adpc_dtype <- bind_rows(adpc_lloq, dtype) %>%
      arrange(STUDYID, USUBJID, BASETYPE, ADTM, NFRLT) %>%
      mutate(
        # Derive baseline flag for pre-dose records
        ABLFL = case_when(
          ATPT == "Pre-dose" & !is.na(AVAL) ~ "Y",
          TRUE ~ NA_character_
        ),
        # Derive MRRLT, ANL01FL and ANL02FL
        MRRLT = if_else(ARRLT < 0, 0, ARRLT),
        ANL01FL = "Y",
        ANL02FL = if_else(is.na(DTYPE), "Y", NA_character_)
      )

STUDYID | USUBJID | ABLFL | BASETYPE | DTYPE | ADTM | ATPT | NFRLT | NRRLT | ARRLT | MRRLT
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
CDISCPILOT01 | 01-701-1028 | Y | Day 1 Baseline | NA | 2013-07-18 23:30:00 | Pre-dose | 0.00 | 0.00 | -0.5000000 | 0.0000000
CDISCPILOT01 | 01-701-1028 | NA | Day 1 Baseline | NA | 2013-07-19 00:00:00 | Dose | 0.00 | 0.00 | 0.0000000 | 0.0000000
CDISCPILOT01 | 01-701-1028 | NA | Day 1 Baseline | NA | 2013-07-19 00:05:00 | 5 Min Post-dose | 0.08 | 0.08 | 0.0833333 | 0.0833333
CDISCPILOT01 | 01-701-1028 | NA | Day 1 Baseline | NA | 2013-07-19 00:30:00 | 30 Min Post-dose | 0.50 | 0.50 | 0.5000000 | 0.5000000
CDISCPILOT01 | 01-701-1028 | NA | Day 1 Baseline | NA | 2013-07-19 01:00:00 | 1h Post-dose | 1.00 | 1.00 | 1.0000000 | 1.0000000
CDISCPILOT01 | 01-701-1028 | NA | Day 1 Baseline | NA | 2013-07-19 01:30:00 | 1.5h Post-dose | 1.50 | 1.50 | 1.5000000 | 1.5000000
CDISCPILOT01 | 01-701-1028 | NA | Day 1 Baseline | NA | 2013-07-19 02:00:00 | 2h Post-dose | 2.00 | 2.00 | 2.0000000 | 2.0000000
CDISCPILOT01 | 01-701-1028 | NA | Day 1 Baseline | NA | 2013-07-19 04:00:00 | 4h Post-dose | 4.00 | 4.00 | 4.0000000 | 4.0000000
CDISCPILOT01 | 01-701-1028 | NA | Day 1 Baseline | NA | 2013-07-19 06:00:00 | 0-6h Post-dose | 3.00 | 3.00 | 6.0000000 | 6.0000000
CDISCPILOT01 | 01-701-1028 | NA | Day 1 Baseline | NA | 2013-07-19 06:00:00 | 6h Post-dose | 6.00 | 6.00 | 6.0000000 | 6.0000000

## Calculate Change from Baseline and Assign `ASEQ`

The `{admiral}` function `derive_var_base()` is used to derive `BASE` and the function `derive_var_chg()` is used to derive change from baseline `CHG`.

We also now derive `ASEQ` using `derive_var_obs_number()` and we drop intermediate variables such as those ending with “_prev” and “_next”.

Finally we derive `PARAM` and `PARAMN` from a lookup table.

    # ---- Derive BASE and Calculate Change from Baseline ----

    adpc_base <- adpc_dtype %>%
      # Derive BASE
      derive_var_base(
        by_vars = exprs(STUDYID, USUBJID, PARAMCD, PARCAT1, BASETYPE),
        source_var = AVAL,
        new_var = BASE,
        filter = ABLFL == "Y"
      )

    # Calculate CHG for post-baseline records
    # The decision on how to populate pre-baseline and baseline values of CHG is left to producer choice
    adpc_chg <- restrict_derivation(
      adpc_base,
      derivation = derive_var_chg,
      filter = AVISITN > 0
    )

    # ---- Add ASEQ ----

    adpc_aseq <- adpc_chg %>%
      # Calculate ASEQ
      derive_var_obs_number(
        new_var = ASEQ,
        by_vars = exprs(STUDYID, USUBJID),
        order = exprs(ADTM, BASETYPE, EVID, AVISITN, ATPTN, PARCAT1, DTYPE),
        check_type = "error"
      ) %>%
      # Remove temporary variables
      select(
        -DOMAIN, -PCSEQ, -starts_with("orig"), -starts_with("min"),
        -starts_with("max"), -starts_with("EX"), -ends_with("next"),
        -ends_with("prev"), -DRUG, -EVID, -AXRLT, -NXRLT, -VISITDY
      ) %>%
      # Derive PARAM and PARAMN
      derive_vars_merged(
        dataset_add = select(param_lookup, -PCTESTCD), by_vars = exprs(PARAMCD)
      )

USUBJID | BASETYPE | DTYPE | AVISIT | ATPT | AVAL | NFRLT | NRRLT | AFRLT | ARRLT | BASE | CHG
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
01-701-1028 | Day 1 Baseline | NA | Day 1 | Pre-dose | 0.0000000 | 0.00 | 0.00 | -0.5000000 | -0.5000000 | 0 | 0.0000000
01-701-1028 | Day 1 Baseline | NA | Day 1 | Dose | 54.0000000 | 0.00 | 0.00 | 0.0000000 | 0.0000000 | NA | NA
01-701-1028 | Day 1 Baseline | NA | Day 1 | 5 Min Post-dose | 0.1015662 | 0.08 | 0.08 | 0.0833333 | 0.0833333 | 0 | 0.1015662
01-701-1028 | Day 1 Baseline | NA | Day 1 | 30 Min Post-dose | 0.5469018 | 0.50 | 0.50 | 0.5000000 | 0.5000000 | 0 | 0.5469018
01-701-1028 | Day 1 Baseline | NA | Day 1 | 1h Post-dose | 0.9254654 | 1.00 | 1.00 | 1.0000000 | 1.0000000 | 0 | 0.9254654
01-701-1028 | Day 1 Baseline | NA | Day 1 | 1.5h Post-dose | 1.1875059 | 1.50 | 1.50 | 1.5000000 | 1.5000000 | 0 | 1.1875059
01-701-1028 | Day 1 Baseline | NA | Day 1 | 2h Post-dose | 1.3688894 | 2.00 | 2.00 | 2.0000000 | 2.0000000 | 0 | 1.3688894
01-701-1028 | Day 1 Baseline | NA | Day 1 | 4h Post-dose | 1.6831476 | 4.00 | 4.00 | 4.0000000 | 4.0000000 | 0 | 1.6831476
01-701-1028 | Day 1 Baseline | NA | Day 1 | 0-6h Post-dose | 24.9423721 | 3.00 | 3.00 | 6.0000000 | 6.0000000 | NA | NA
01-701-1028 | Day 1 Baseline | NA | Day 1 | 6h Post-dose | 1.7552923 | 6.00 | 6.00 | 6.0000000 | 6.0000000 | 0 | 1.7552923

## Add Additional Baseline Variables

Here we derive additional baseline values from `VS` for baseline height `HTBL` and weight `WTBL` and compute the body mass index (BMI) with `compute_bmi()`. These values could also be obtained from `ADVS` if available. Baseline lab values could also be derived from `LB` or `ADLB` in a similar manner.

    # Derive additional baselines from VS
    adpc_baselines <- adpc_aseq %>%
      derive_vars_merged(
        dataset_add = vs,
        filter_add = VSTESTCD == "HEIGHT",
        by_vars = exprs(STUDYID, USUBJID),
        new_vars = exprs(HTBL = VSSTRESN, HTBLU = VSSTRESU)
      ) %>%
      derive_vars_merged(
        dataset_add = vs,
        filter_add = VSTESTCD == "WEIGHT" & VSBLFL == "Y",
        by_vars = exprs(STUDYID, USUBJID),
        new_vars = exprs(WTBL = VSSTRESN, WTBLU = VSSTRESU)
      ) %>%
      mutate(
        BMIBL = compute_bmi(height = HTBL, weight = WTBL),
        BMIBLU = "kg/m^2"
      )

USUBJID | HTBL | HTBLU | WTBL | WTBLU | BMIBL | BMIBLU | BASETYPE | ATPT | AVAL
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
01-701-1028 | 177.8 | cm | 99.34 | kg | 31.42394 | kg/m^2 | Day 1 Baseline | Pre-dose | 0.0000000
01-701-1028 | 177.8 | cm | 99.34 | kg | 31.42394 | kg/m^2 | Day 1 Baseline | Dose | 54.0000000
01-701-1028 | 177.8 | cm | 99.34 | kg | 31.42394 | kg/m^2 | Day 1 Baseline | 5 Min Post-dose | 0.1015662
01-701-1028 | 177.8 | cm | 99.34 | kg | 31.42394 | kg/m^2 | Day 1 Baseline | 30 Min Post-dose | 0.5469018
01-701-1028 | 177.8 | cm | 99.34 | kg | 31.42394 | kg/m^2 | Day 1 Baseline | 1h Post-dose | 0.9254654
01-701-1028 | 177.8 | cm | 99.34 | kg | 31.42394 | kg/m^2 | Day 1 Baseline | 1.5h Post-dose | 1.1875059
01-701-1028 | 177.8 | cm | 99.34 | kg | 31.42394 | kg/m^2 | Day 1 Baseline | 2h Post-dose | 1.3688894
01-701-1028 | 177.8 | cm | 99.34 | kg | 31.42394 | kg/m^2 | Day 1 Baseline | 4h Post-dose | 1.6831476
01-701-1028 | 177.8 | cm | 99.34 | kg | 31.42394 | kg/m^2 | Day 1 Baseline | 0-6h Post-dose | 24.9423721
01-701-1028 | 177.8 | cm | 99.34 | kg | 31.42394 | kg/m^2 | Day 1 Baseline | 6h Post-dose | 1.7552923

## Add the `ADSL` variables

If needed, the other `ADSL` variables can now be added:

    # Add all ADSL variables
    adpc <- adpc_baselines %>%
      derive_vars_merged(
        dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
        by_vars = exprs(STUDYID, USUBJID)
      )

Adding attributes to the `ADPC` file will be discussed below. We will now turn to the Population PK example.

# Programming Population PK (ADPPK) Analysis Data

The Population PK Analysis Data (ADPPK) follows the CDISC Implementation Guide (https://www.cdisc.org/standards/foundational/adam/basic-data-structure-adam-poppk-implementation-guide-v1-0). The programming workflow for Population PK (ADPPK) Analysis Data is similar to the NCA Programming flow with a few key differences. Population PK models generally make use of nonlinear mixed effects models that require numeric variables. The data used in the models will include both dosing and concentration records, relative time variables, and numeric covariate variables. A `DV` or dependent variable is often expected. This is equivalent to the ADaM `AVAL` variable and will be included in addition to `AVAL` for ADPPK. The ADPPK file will not have the duplicated records for analysis found in the NCA.

Here are the relative time variables we will use for the ADPPK data. These correspond to the names in the forthcoming CDISC Implementation Guide.

Variable | Variable Label
--- | ---
NFRLT | Nominal Rel Time from First Dose
AFRLT | Actual Rel Time from First Dose
NPRLT | Nominal Rel Time from Previous Dose
APRLT | Actual Rel Time from Previous Dose

The `ADPPK` will require the numeric Event ID (`EVID`) which we defined in `ADPC` but did not keep.

# `ADPPK` Programming Workflow

- Read in Data (Same as `ADPC`)

- Expand Dosing Records (Same as `ADPC`)

- Find First Dose

- Find Previous Dose

- Combine PC and EX Records for `ADPPK`

- Derive Analysis Variables and Dependent Variable `DV`

- Add `ASEQ` and Remove Temporary Variables

- Derive Numeric Covariates

- Derive Additional Covariates from VS and LB

- Combine Covariates with `ADPPK` Data

## Find First Dose `ADPPK`

The initial programming steps for `ADPPK` will follow the same sequence as the `ADPC`. This includes reading in the `{pharmaversesdtm}` data, deriving analysis dates, defining the nominal relative time from first dose `NFRLT`, and expanding dosing records. For more detail see these steps above (Read in Data).

We will pick this up at the stage where we find the first dose for the concentration records. We will use `derive_vars_merged()` as we did for `ADPC`.

    # ---- Find first dose per treatment per subject ----
    # ---- Join with ADPC data and keep only subjects with dosing ----

    adppk_first_dose <- pc_dates %>%
      derive_vars_merged(
        dataset_add = ex_exp,
        filter_add = (!is.na(ADTM)),
        new_vars = exprs(FANLDTM = ADTM, EXDOSE_first = EXDOSE),
        order = exprs(ADTM, EXSEQ),
        mode = "first",
        by_vars = exprs(STUDYID, USUBJID, DRUG)
      ) %>%
      filter(!is.na(FANLDTM)) %>%
      # Derive AVISIT based on nominal relative time
      # Derive AVISITN to nominal time in whole days using integer division
      # Define AVISIT based on nominal day
      mutate(
        AVISITN = NFRLT %/% 24 + 1,
        AVISIT = paste("Day", AVISITN),
      )

USUBJID | FANLDTM | AVISIT | ADTM | PCTPT
--- | --- | --- | --- | ---
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-18 23:30:00 | Pre-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 00:05:00 | 5 Min Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 00:30:00 | 30 Min Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 01:00:00 | 1h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 01:30:00 | 1.5h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 02:00:00 | 2h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 04:00:00 | 4h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 06:00:00 | 6h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 08:00:00 | 8h Post-dose
01-701-1028 | 2013-07-19 | Day 1 | 2013-07-19 12:00:00 | 12h Post-dose

## Find Previous Dose

For `ADPPK` we will find the previous dose with respect to actual time and nominal time. We will use `derive_vars_joined()` as we did for `ADPC`, but note that we will not need to find the next dose as for `ADPC`.

    # ---- Find previous dose  ----

    adppk_prev <- adppk_first_dose %>%
      derive_vars_joined(
        dataset_add = ex_exp,
        by_vars = exprs(USUBJID),
        order = exprs(ADTM),
        new_vars = exprs(
          ADTM_prev = ADTM, EXDOSE_prev = EXDOSE, AVISIT_prev = AVISIT,
          AENDTM_prev = AENDTM
        ),
        join_vars = exprs(ADTM),
        join_type = "all",
        filter_add = NULL,
        filter_join = ADTM > ADTM.join,
        mode = "last",
        check_type = "none"
      )

    # ---- Find previous nominal dose ----

    adppk_nom_prev <- adppk_prev %>%
      derive_vars_joined(
        dataset_add = ex_exp,
        by_vars = exprs(USUBJID),
        order = exprs(NFRLT),
        new_vars = exprs(NFRLT_prev = NFRLT),
        join_vars = exprs(NFRLT),
        join_type = "all",
        filter_add = NULL,
        filter_join = NFRLT > NFRLT.join,
        mode = "last",
        check_type = "none"
      )

USUBJID | VISIT | ADTM | PCTPT | ADTM_prev | NFRLT_prev
--- | --- | --- | --- | --- | ---
01-701-1028 | BASELINE | 2013-07-18 23:30:00 | Pre-dose | NA | NA
01-701-1028 | BASELINE | 2013-07-19 00:05:00 | 5 Min Post-dose | 2013-07-19 | 0
01-701-1028 | BASELINE | 2013-07-19 00:30:00 | 30 Min Post-dose | 2013-07-19 | 0
01-701-1028 | BASELINE | 2013-07-19 01:00:00 | 1h Post-dose | 2013-07-19 | 0
01-701-1028 | BASELINE | 2013-07-19 01:30:00 | 1.5h Post-dose | 2013-07-19 | 0
01-701-1028 | BASELINE | 2013-07-19 02:00:00 | 2h Post-dose | 2013-07-19 | 0
01-701-1028 | BASELINE | 2013-07-19 04:00:00 | 4h Post-dose | 2013-07-19 | 0
01-701-1028 | BASELINE | 2013-07-19 06:00:00 | 6h Post-dose | 2013-07-19 | 0
01-701-1028 | BASELINE | 2013-07-19 08:00:00 | 8h Post-dose | 2013-07-19 | 0
01-701-1028 | BASELINE | 2013-07-19 12:00:00 | 12h Post-dose | 2013-07-19 | 0

## Combine PC and EX Records for `ADPPK`

As we did for `ADPC` we will now combine `PC` and `EX` records. We will derive the relative time variables `AFRLT` (Actual Relative Time from First Dose), `APRLT` (Actual Relative Time from Previous Dose), and `NPRLT` (Nominal Relative Time from Previous Dose). Use `derive_vars_duration()` to derive `AFRLT` and `APRLT`. Note we defined `EVID` above with values of 0 for observation records and 1 for dosing records.

    # ---- Combine ADPPK and EX data ----
    # Derive Relative Time Variables

    adppk_aprlt <- bind_rows(adppk_nom_prev, ex_exp) %>%
      group_by(USUBJID, DRUG) %>%
      mutate(
        FANLDTM = min(FANLDTM, na.rm = TRUE),
        min_NFRLT = min(NFRLT, na.rm = TRUE),
        maxdate = max(ADT[EVID == 0], na.rm = TRUE), .after = USUBJID
      ) %>%
      arrange(USUBJID, ADTM) %>%
      ungroup() %>%
      filter(ADT <= maxdate) %>%
      # Derive Actual Relative Time from First Dose (AFRLT)
      derive_vars_duration(
        new_var = AFRLT,
        start_date = FANLDTM,
        end_date = ADTM,
        out_unit = "hours",
        floor_in = FALSE,
        add_one = FALSE
      ) %>%
      # Derive Actual Relative Time from Reference Dose (APRLT)
      derive_vars_duration(
        new_var = APRLT,
        start_date = ADTM_prev,
        end_date = ADTM,
        out_unit = "hours",
        floor_in = FALSE,
        add_one = FALSE
      ) %>%
      # Derive APRLT
      mutate(
        APRLT = case_when(
          EVID == 1 ~ 0,
          is.na(APRLT) ~ AFRLT,
          TRUE ~ APRLT
        ),
        NPRLT = case_when(
          EVID == 1 ~ 0,
          is.na(NFRLT_prev) ~ NFRLT - min_NFRLT,
          TRUE ~ NFRLT - NFRLT_prev
        )
      )

USUBJID | EVID | NFRLT | AFRLT | APRLT | NPRLT
--- | --- | --- | --- | --- | ---
01-701-1028 | 0 | 0.00 | -0.5000000 | -0.5000000 | 0.00
01-701-1028 | 1 | 0.00 | 0.0000000 | 0.0000000 | 0.00
01-701-1028 | 0 | 0.08 | 0.0833333 | 0.0833333 | 0.08
01-701-1028 | 0 | 0.50 | 0.5000000 | 0.5000000 | 0.50
01-701-1028 | 0 | 1.00 | 1.0000000 | 1.0000000 | 1.00
01-701-1028 | 0 | 1.50 | 1.5000000 | 1.5000000 | 1.50
01-701-1028 | 0 | 2.00 | 2.0000000 | 2.0000000 | 2.00
01-701-1028 | 0 | 4.00 | 4.0000000 | 4.0000000 | 4.00
01-701-1028 | 0 | 6.00 | 6.0000000 | 6.0000000 | 6.00
01-701-1028 | 0 | 3.00 | 6.0000000 | 6.0000000 | 3.00

## Derive Analysis Variables and Dependent Variable `DV`

The expected analysis variable for `ADPPK` is `DV` or dependent variable. For this example `DV` is set to the numeric concentration value `PCSTRESN`. We will also include `AVAL` equivalent to `DV` for consistency with CDISC ADaM standards. `MDV` missing dependent variable will also be included.

    # ---- Derive Analysis Variables ----
    # Derive actual dose DOSEA and planned dose DOSEP,
    # Derive AVAL and DV

    adppk_aval <- adppk_aprlt %>%
      mutate(
        # Derive Actual Dose
        DOSEA = case_when(
          EVID == 1 ~ EXDOSE,
          is.na(EXDOSE_prev) ~ EXDOSE_first,
          TRUE ~ EXDOSE_prev
        ),
        # Derive Planned Dose
        DOSEP = case_when(
          TRT01P == "Xanomeline High Dose" ~ 81,
          TRT01P == "Xanomeline Low Dose" ~ 54,
          TRT01P == "Placebo" ~ 0
        ),
        # Derive PARAMCD
        PARAMCD = case_when(
          EVID == 1 ~ "DOSE",
          TRUE ~ PCTESTCD
        ),
        ALLOQ = PCLLOQ,
        # Derive CMT
        CMT = case_when(
          EVID == 1 ~ 1,
          PCSPEC == "PLASMA" ~ 2,
          TRUE ~ 3
        ),
        # Derive BLQFL/BLQFN
        BLQFL = case_when(
          PCSTRESC == "<BLQ" ~ "Y",
          TRUE ~ "N"
        ),
        BLQFN = case_when(
          PCSTRESC == "<BLQ" ~ 1,
          TRUE ~ 0
        ),
        AMT = case_when(
          EVID == 1 ~ EXDOSE,
          TRUE ~ NA_real_
        ),
        # Derive DV and AVAL
        DV = PCSTRESN,
        AVAL = DV,
        DVL = case_when(
          DV != 0 ~ log(DV),
          TRUE ~ NA_real_
        ),
        # Derive MDV
        MDV = case_when(
          EVID == 1 ~ 1,
          is.na(DV) ~ 1,
          TRUE ~ 0
        ),
        AVALU = case_when(
          EVID == 1 ~ NA_character_,
          TRUE ~ PCSTRESU
        ),
        UDTC = format_ISO8601(ADTM),
        II = if_else(EVID == 1, 1, 0),
        SS = if_else(EVID == 1, 1, 0)
      )

USUBJID | EVID | DOSEA | AMT | NFRLT | AFRLT | CMT | DV | MDV | BLQFN
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
01-701-1028 | 0 | 54 | NA | 0.00 | -0.5000000 | 2 | 0.0000000 | 0 | 1
01-701-1028 | 1 | 54 | 54 | 0.00 | 0.0000000 | 1 | NA | 1 | 0
01-701-1028 | 0 | 54 | NA | 0.08 | 0.0833333 | 2 | 0.1015662 | 0 | 0
01-701-1028 | 0 | 54 | NA | 0.50 | 0.5000000 | 2 | 0.5469018 | 0 | 0
01-701-1028 | 0 | 54 | NA | 1.00 | 1.0000000 | 2 | 0.9254654 | 0 | 0
01-701-1028 | 0 | 54 | NA | 1.50 | 1.5000000 | 2 | 1.1875059 | 0 | 0
01-701-1028 | 0 | 54 | NA | 2.00 | 2.0000000 | 2 | 1.3688894 | 0 | 0
01-701-1028 | 0 | 54 | NA | 4.00 | 4.0000000 | 2 | 1.6831476 | 0 | 0
01-701-1028 | 0 | 54 | NA | 6.00 | 6.0000000 | 2 | 1.7552923 | 0 | 0
01-701-1028 | 0 | 54 | NA | 3.00 | 6.0000000 | 3 | 24.9423721 | 0 | 0

## Add `ASEQ` and Remove Temporary Variables

We derive `ASEQ` using `derive_var_obs_number()`. We add a `PROJID` based on drug and include the numeric version `PROJIDN`, and we drop and we drop intermediate variables such as those ending with “_prev”.

    # ---- Add ASEQ ----

    adppk_aseq <- adppk_aval %>%
      # Calculate ASEQ
      derive_var_obs_number(
        new_var = ASEQ,
        by_vars = exprs(STUDYID, USUBJID),
        order = exprs(AFRLT, EVID, CMT),
        check_type = "error"
      ) %>%
      # Derive PARAM and PARAMN
      derive_vars_merged(dataset_add = select(param_lookup, -PCTESTCD), by_vars = exprs(PARAMCD)) %>%
      mutate(
        PROJID = DRUG,
        PROJIDN = 1
      ) %>%
      # Remove temporary variables
      select(
        -DOMAIN, -starts_with("min"), -starts_with("max"), -starts_with("EX"),
        -starts_with("PC"), -ends_with("first"), -ends_with("prev"),
        -ends_with("DTM"), -ends_with("DT"), -ends_with("TM"), -starts_with("VISIT"),
        -starts_with("AVISIT"), -ends_with("TMF"), -starts_with("TRT"),
        -starts_with("ATPT"), -DRUG
      )

USUBJID | EVID | DOSEA | AMT | NFRLT | AFRLT | CMT | DV | MDV | BLQFN | ASEQ
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
01-701-1028 | 0 | 54 | NA | 0.00 | -0.5000000 | 2 | 0.0000000 | 0 | 1 | 1
01-701-1028 | 1 | 54 | 54 | 0.00 | 0.0000000 | 1 | NA | 1 | 0 | 2
01-701-1028 | 0 | 54 | NA | 0.08 | 0.0833333 | 2 | 0.1015662 | 0 | 0 | 3
01-701-1028 | 0 | 54 | NA | 0.50 | 0.5000000 | 2 | 0.5469018 | 0 | 0 | 4
01-701-1028 | 0 | 54 | NA | 1.00 | 1.0000000 | 2 | 0.9254654 | 0 | 0 | 5
01-701-1028 | 0 | 54 | NA | 1.50 | 1.5000000 | 2 | 1.1875059 | 0 | 0 | 6
01-701-1028 | 0 | 54 | NA | 2.00 | 2.0000000 | 2 | 1.3688894 | 0 | 0 | 7
01-701-1028 | 0 | 54 | NA | 4.00 | 4.0000000 | 2 | 1.6831476 | 0 | 0 | 8
01-701-1028 | 0 | 54 | NA | 6.00 | 6.0000000 | 2 | 1.7552923 | 0 | 0 | 9
01-701-1028 | 0 | 54 | NA | 3.00 | 6.0000000 | 3 | 24.9423721 | 0 | 0 | 10

## Derive Numeric Covariates

A key feature of Population PK modeling is the presence of numeric covariates. We will create numeric versions of many of our standard CDISC demographic variables including `STUDYIDN`, `USUBJIDN`, `SEXN`, `RACEN`, and `ETHNICN`.

    #---- Derive Covariates ----
    # Include numeric values for STUDYIDN, USUBJIDN, SEXN, RACEN etc.

    covar <- adsl %>%
      derive_vars_merged(
        dataset_add = country_code_lookup,
        new_vars = exprs(COUNTRYN = country_number, COUNTRYL = country_name),
        by_vars = exprs(COUNTRY = country_code),
      ) %>%
      mutate(
        STUDYIDN = as.numeric(word(USUBJID, 1, sep = fixed("-"))),
        SITEIDN = as.numeric(word(USUBJID, 2, sep = fixed("-"))),
        USUBJIDN = as.numeric(word(USUBJID, 3, sep = fixed("-"))),
        SUBJIDN = as.numeric(SUBJID),
        SEXN = case_when(
          SEX == "M" ~ 1,
          SEX == "F" ~ 2,
          TRUE ~ 3
        ),
        RACEN = case_when(
          RACE == "AMERICAN INDIAN OR ALASKA NATIVE" ~ 1,
          RACE == "ASIAN" ~ 2,
          RACE == "BLACK OR AFRICAN AMERICAN" ~ 3,
          RACE == "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER" ~ 4,
          RACE == "WHITE" ~ 5,
          TRUE ~ 6
        ),
        ETHNICN = case_when(
          ETHNIC == "HISPANIC OR LATINO" ~ 1,
          ETHNIC == "NOT HISPANIC OR LATINO" ~ 2,
          TRUE ~ 3
        ),
        ARMN = case_when(
          ARM == "Placebo" ~ 0,
          ARM == "Xanomeline Low Dose" ~ 1,
          ARM == "Xanomeline High Dose" ~ 2,
          TRUE ~ 3
        ),
        ACTARMN = case_when(
          ACTARM == "Placebo" ~ 0,
          ACTARM == "Xanomeline Low Dose" ~ 1,
          ACTARM == "Xanomeline High Dose" ~ 2,
          TRUE ~ 3
        ),
        COHORT = ARMN,
        COHORTC = ARM,
        ROUTE = unique(ex$EXROUTE),
        ROUTEN = case_when(
          ROUTE == "TRANSDERMAL" ~ 3,
          TRUE ~ NA_real_
        ),
        FORM = unique(ex$EXDOSFRM),
        FORMN = case_when(
          FORM == "PATCH" ~ 3,
          TRUE ~ 4
        )
      ) %>%
      select(
        STUDYID, STUDYIDN, SITEID, SITEIDN, USUBJID, USUBJIDN,
        SUBJID, SUBJIDN, AGE, SEX, SEXN, COHORT, COHORTC, ROUTE, ROUTEN,
        RACE, RACEN, ETHNIC, ETHNICN, FORM, FORMN, COUNTRY, COUNTRYN, COUNTRYL
      )

STUDYIDN | USUBJIDN | SITEIDN | COUNTRY | COUNTRYN | AGE | SEXN | RACEN | COHORT | ROUTEN
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
1 | 1015 | 701 | USA | 235 | 63 | 2 | 5 | 0 | 3
1 | 1023 | 701 | USA | 235 | 64 | 1 | 5 | 0 | 3
1 | 1028 | 701 | USA | 235 | 71 | 1 | 5 | 2 | 3
1 | 1033 | 701 | USA | 235 | 74 | 1 | 5 | 1 | 3
1 | 1034 | 701 | USA | 235 | 77 | 2 | 5 | 2 | 3
1 | 1047 | 701 | USA | 235 | 85 | 2 | 5 | 0 | 3
1 | 1057 | 701 | USA | 235 | 59 | 2 | 5 | 3 | 3
1 | 1097 | 701 | USA | 235 | 68 | 1 | 5 | 1 | 3
1 | 1111 | 701 | USA | 235 | 81 | 2 | 5 | 1 | 3
1 | 1115 | 701 | USA | 235 | 84 | 1 | 5 | 1 | 3

## Derive Additional Covariates from VS and LB

We will add additional covariates for baseline height `HTBL` and weight `WTBL` from `VS` and select baseline lab values `CREATBL`, `ALTBL`, `ASTBL` and `TBILBL` from `LB`. We will calculate BMI and BSA from height and weight using `compute_bmi()` and `compute_bsa()`. And we will calculate creatinine clearance `CRCLBL` and estimated glomerular filtration rate (eGFR) `EGFRBL` using `compute_egfr()` function.

    #---- Derive additional baselines from VS and LB ----

    labsbl <- lb %>%
      filter(LBBLFL == "Y" & LBTESTCD %in% c("CREAT", "ALT", "AST", "BILI")) %>%
      mutate(LBTESTCDB = paste0(LBTESTCD, "BL")) %>%
      select(STUDYID, USUBJID, LBTESTCDB, LBSTRESN)

    covar_vslb <- covar %>%
      derive_vars_merged(
        dataset_add = vs,
        filter_add = VSTESTCD == "HEIGHT",
        by_vars = exprs(STUDYID, USUBJID),
        new_vars = exprs(HTBL = VSSTRESN)
      ) %>%
      derive_vars_merged(
        dataset_add = vs,
        filter_add = VSTESTCD == "WEIGHT" & VSBLFL == "Y",
        by_vars = exprs(STUDYID, USUBJID),
        new_vars = exprs(WTBL = VSSTRESN)
      ) %>%
      derive_vars_transposed(
        dataset_merge = labsbl,
        by_vars = exprs(STUDYID, USUBJID),
        key_var = LBTESTCDB,
        value_var = LBSTRESN
      ) %>%
      mutate(
        BMIBL = compute_bmi(height = HTBL, weight = WTBL),
        BSABL = compute_bsa(
          height = HTBL,
          weight = HTBL,
          method = "Mosteller"
        ),
        # Derive CRCLBL and EGFRBL using new function
        CRCLBL = compute_egfr(
          creat = CREATBL, creatu = "SI", age = AGE, weight = WTBL, sex = SEX,
          method = "CRCL"
        ),
        EGFRBL = compute_egfr(
          creat = CREATBL, creatu = "SI", age = AGE, weight = WTBL, sex = SEX,
          method = "CKD-EPI"
        )
      ) %>%
      rename(TBILBL = BILIBL)

USUBJIDN | AGE | SEXN | HTBL | WTBL | CREATBL | ALTBL | ASTBL
--- | --- | --- | --- | --- | --- | --- | ---
1015 | 63 | 2 | 147.32 | 54.43 | 79.56 | 27 | 40
1023 | 64 | 1 | 162.56 | 80.29 | 123.76 | 23 | 21
1028 | 71 | 1 | 177.80 | 99.34 | 123.76 | 26 | 24
1033 | 74 | 1 | 175.26 | 88.45 | 132.60 | 16 | 20
1034 | 77 | 2 | 154.94 | 62.60 | 88.40 | 15 | 23
1047 | 85 | 2 | 148.59 | 67.13 | 88.40 | 22 | 25
1057 | 59 | 2 | NA | NA | NA | NA | NA
1097 | 68 | 1 | 168.91 | 78.02 | 123.76 | 16 | 19
1111 | 81 | 2 | 158.24 | 59.88 | 79.56 | 23 | 28
1115 | 84 | 1 | 181.61 | 78.93 | 114.92 | 18 | 26

## Combine Covariates with `ADPPK` Data

Finally, we combine the covariates with the `ADPPK` data.

    # Combine covariates with APPPK data

    adppk <- adppk_aseq %>%
      derive_vars_merged(
        dataset_add = covar_vslb,
        by_vars = exprs(STUDYID, USUBJID)
      ) %>%
      arrange(STUDYIDN, USUBJIDN, AFRLT, EVID) %>%
      mutate(RECSEQ = row_number())

USUBJIDN | AGE | SEXN | CREATBL | EVID | AMT | DV | MDV | RECSEQ
--- | --- | --- | --- | --- | --- | --- | --- | ---
1028 | 71 | 1 | 123.76 | 0 | NA | 0.0000000 | 0 | 1
1028 | 71 | 1 | 123.76 | 1 | 54 | NA | 1 | 2
1028 | 71 | 1 | 123.76 | 0 | NA | 0.1015662 | 0 | 3
1028 | 71 | 1 | 123.76 | 0 | NA | 0.5469018 | 0 | 4
1028 | 71 | 1 | 123.76 | 0 | NA | 0.9254654 | 0 | 5
1028 | 71 | 1 | 123.76 | 0 | NA | 1.1875059 | 0 | 6
1028 | 71 | 1 | 123.76 | 0 | NA | 1.3688894 | 0 | 7
1028 | 71 | 1 | 123.76 | 0 | NA | 1.6831476 | 0 | 8
1028 | 71 | 1 | 123.76 | 0 | NA | 1.7552923 | 0 | 9
1028 | 71 | 1 | 123.76 | 0 | NA | 24.9423721 | 0 | 10

# 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 Scripts

ADaM | Sourcing Command
--- | ---
ADPC | `use_ad_template("ADPC")`
ADPPK | `use_ad_template("ADPPK")`