In Roche-GSK/admiral: ADaM in R Asset Library

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(admiraldev)

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 {#readdata}

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,
)

ex <- filter(ex, USUBJID %in% c(
  "01-701-1028", "01-701-1033", "01-701-1442", "01-714-1288", "01-718-1101"
))
pc <- filter(pc, USUBJID %in% c(
  "01-701-1028", "01-701-1033", "01-701-1442", "01-714-1288", "01-718-1101"
))

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
  )

dataset_vignette(
  pc_dates,
  display_vars = exprs(
    USUBJID, PCTEST, ADTM, VISIT, PCTPT, NFRLT
  )
)

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))

dataset_vignette(
  ex_dates,
  display_vars = exprs(
    USUBJID, EXTRT, EXDOSFRQ, ASTDTM, AENDTM, VISIT, VISITDY, NFRLT
  )
)

Expand Dosing Records {#expand}

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))

dataset_vignette(
  ex_exp,
  display_vars = exprs(
    USUBJID, DRUG, EXDOSFRQ, ASTDTM, AENDTM, AVISIT, NFRLT
  )
)

Find First Dose {#firstdose}

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)
  )

dataset_vignette(
  adpc_first_dose,
  display_vars = exprs(
    USUBJID, FANLDTM, AVISIT, ADTM, PCTPT
  )
)

Find Reference Dose Dates Corresponding to PK Records {#dosedates}

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"
  )

dataset_vignette(
  adpc_prev,
  display_vars = exprs(
    USUBJID, VISIT, ADTM, VISIT, PCTPT, ADTM_prev, EXDOSE_prev, AVISIT_prev
  )
)

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"
  )

dataset_vignette(
  adpc_next,
  display_vars = exprs(
    USUBJID,
    VISIT, ADTM, VISIT, PCTPT, ADTM_next, EXDOSE_next, AVISIT_next
  )
)

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"
  )

dataset_vignette(
  adpc_nom_next,
  display_vars = exprs(
    USUBJID, NFRLT, PCTPT, NFRLT_prev, NFRLT_next
  )
)

Combine PC and EX Records and Derive Relative Time Variables {#relative}

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))

dataset_vignette(
  adpc_arrlt,
  display_vars = exprs(
    USUBJID, FANLDTM, AVISIT, PCTPT, AFRLT, ARRLT, AXRLT
  )
)

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
    )
  )

dataset_vignette(
  adpc_nrrlt,
  display_vars = exprs(
    USUBJID, AVISIT, PCTPT, NFRLT, NRRLT, NXRLT
  )
)

Derive Analysis Variables {#analysis}

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)
  )

dataset_vignette(
  adpc_aval,
  display_vars = exprs(
    USUBJID, NFRLT, AVISIT, ATPT, ATPTREF, AVAL, AVALCAT1
  )
)

Create `DTYPE` for Imputed records {#lloq}

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"
    )
  )

dataset_vignette(
  tail(adpc_lloq),
  display_vars = exprs(
    USUBJID, PARAMCD, PARCAT1, AVISITN, DTYPE, PCSTRESC, AVAL,
  )
)

Create Duplicated Records for Analysis {#copy}

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))

dataset_vignette(
  dtype,
  display_vars = exprs(
    USUBJID, DTYPE, ATPT, NFRLT, NRRLT, AFRLT, ARRLT, BASETYPE
  )
)

Combine `ADPC` data with Duplicated Records {#combine}

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_)
  )

adpc_dtype %>%
  dataset_vignette(display_vars = exprs(
    STUDYID, USUBJID, ABLFL, BASETYPE, DTYPE, ADTM, ATPT, NFRLT, NRRLT, ARRLT, MRRLT
  ))

Calculate Change from Baseline and Assign `ASEQ` {#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)
  )

adpc_aseq %>%
  dataset_vignette(display_vars = exprs(
    USUBJID, BASETYPE, DTYPE, AVISIT, ATPT, AVAL, NFRLT, NRRLT, AFRLT, ARRLT, BASE, CHG
  ))

Add Additional Baseline Variables {#baselines}

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"
  )

adpc_baselines %>%
  dataset_vignette(display_vars = exprs(
    USUBJID, HTBL, HTBLU, WTBL, WTBLU, BMIBL, BMIBLU, BASETYPE, ATPT, AVAL
  ))

Add the `ADSL` variables {#adsl_vars}

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` {#ppkfirst}

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),
  )

dataset_vignette(
  adppk_first_dose,
  display_vars = exprs(
    USUBJID, FANLDTM, AVISIT, ADTM, PCTPT
  )
)

Find Previous Dose {#prevdose}

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"
  )

dataset_vignette(
  adppk_nom_prev,
  display_vars = exprs(
    USUBJID, VISIT, ADTM, VISIT, PCTPT, ADTM_prev, NFRLT_prev
  )
)

Combine PC and EX Records for `ADPPK` {#aprlt}

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
    )
  )

dataset_vignette(
  adppk_aprlt,
  display_vars = exprs(
    USUBJID, EVID, NFRLT, AFRLT, APRLT, NPRLT
  )
)

Derive Analysis Variables and Dependent Variable `DV` {#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)
  )

dataset_vignette(
  adppk_aval,
  display_vars = exprs(
    USUBJID, EVID, DOSEA, AMT, NFRLT, AFRLT, CMT, DV, MDV, BLQFN
  )
)

Add `ASEQ` and Remove Temporary Variables {#ppkaseq}

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
  )

dataset_vignette(
  adppk_aseq,
  display_vars = exprs(
    USUBJID, EVID, DOSEA, AMT, NFRLT, AFRLT, CMT, DV, MDV, BLQFN, ASEQ
  )
)

Derive Numeric Covariates {#covar}

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
  )

dataset_vignette(
  covar,
  display_vars = exprs(
    STUDYIDN, USUBJIDN, SITEIDN, COUNTRY, COUNTRYN, AGE, SEXN, RACEN, COHORT, ROUTEN
  )
)

Derive Additional Covariates from VS and LB {#addcovar}

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)

dataset_vignette(
  covar_vslb,
  display_vars = exprs(
    USUBJIDN, AGE, SEXN, HTBL, WTBL, CREATBL, ALTBL, ASTBL
  )
)

Combine Covariates with `ADPPK` Data {#final}

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())

dataset_vignette(
  adppk,
  display_vars = exprs(
    USUBJIDN, AGE, SEXN, CREATBL, EVID, AMT, DV, MDV, RECSEQ
  )
)

Add Labels and Attributes {#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 {#example}

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

Roche-GSK/admiral documentation built on April 14, 2025, 12:36 p.m.

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Roche-GSK/admiral
ADaM in R Asset Library

In Roche-GSK/admiral: ADaM in R Asset Library

Introduction

Programming PK NCA (ADPC/ADNCA) Analysis Data

`ADPC` Programming Workflow

Read in Data {#readdata}

Expand Dosing Records {#expand}

Find First Dose {#firstdose}

Find Reference Dose Dates Corresponding to PK Records {#dosedates}

Combine PC and EX Records and Derive Relative Time Variables {#relative}

Derive Analysis Variables {#analysis}

Create `DTYPE` for Imputed records {#lloq}

Create Duplicated Records for Analysis {#copy}

Combine `ADPC` data with Duplicated Records {#combine}

Calculate Change from Baseline and Assign `ASEQ` {#aseq}

Add Additional Baseline Variables {#baselines}

Add the `ADSL` variables {#adsl_vars}

Programming Population PK (ADPPK) Analysis Data

`ADPPK` Programming Workflow

Find First Dose `ADPPK` {#ppkfirst}

Find Previous Dose {#prevdose}

Combine PC and EX Records for `ADPPK` {#aprlt}

Derive Analysis Variables and Dependent Variable `DV` {#dv}

Add `ASEQ` and Remove Temporary Variables {#ppkaseq}

Derive Numeric Covariates {#covar}

Derive Additional Covariates from VS and LB {#addcovar}

Combine Covariates with `ADPPK` Data {#final}

Add Labels and Attributes {#attributes}

Example Scripts {#example}

R Package Documentation

Browse R Packages

We want your feedback!

Roche-GSK/admiral ADaM in R Asset Library

In Roche-GSK/admiral: ADaM in R Asset Library

Introduction

Programming PK NCA (ADPC/ADNCA) Analysis Data

ADPC Programming Workflow

Read in Data {#readdata}

Expand Dosing Records {#expand}

Find First Dose {#firstdose}

Find Reference Dose Dates Corresponding to PK Records {#dosedates}

Combine PC and EX Records and Derive Relative Time Variables {#relative}

Derive Analysis Variables {#analysis}

Create DTYPE for Imputed records {#lloq}

Create Duplicated Records for Analysis {#copy}

Combine ADPC data with Duplicated Records {#combine}

Calculate Change from Baseline and Assign ASEQ {#aseq}

Add Additional Baseline Variables {#baselines}

Add the ADSL variables {#adsl_vars}

Programming Population PK (ADPPK) Analysis Data

ADPPK Programming Workflow

Find First Dose ADPPK {#ppkfirst}

Find Previous Dose {#prevdose}

Combine PC and EX Records for ADPPK {#aprlt}

Derive Analysis Variables and Dependent Variable DV {#dv}

Add ASEQ and Remove Temporary Variables {#ppkaseq}

Derive Numeric Covariates {#covar}

Derive Additional Covariates from VS and LB {#addcovar}

Combine Covariates with ADPPK Data {#final}

Add Labels and Attributes {#attributes}

Example Scripts {#example}

R Package Documentation

Browse R Packages

We want your feedback!

Roche-GSK/admiral
ADaM in R Asset Library

`ADPC` Programming Workflow

Create `DTYPE` for Imputed records {#lloq}

Combine `ADPC` data with Duplicated Records {#combine}

Calculate Change from Baseline and Assign `ASEQ` {#aseq}

Add the `ADSL` variables {#adsl_vars}

`ADPPK` Programming Workflow

Find First Dose `ADPPK` {#ppkfirst}

Combine PC and EX Records for `ADPPK` {#aprlt}

Derive Analysis Variables and Dependent Variable `DV` {#dv}

Add `ASEQ` and Remove Temporary Variables {#ppkaseq}

Combine Covariates with `ADPPK` Data {#final}