ADPPK

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). 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. For more details see the {admiral} vignette.

First Load Packages

First we will load the packages required for our project. We will use {admiral} for the creation of analysis data. {admiral} requires {dplyr}, {lubridate} and {stringr}. We will use {metacore} and {metatools} to store and manipulate metadata from our specifications. We will use {xportr} to perform checks on the final data and export to a transport file.

The source SDTM data will come from the CDISC pilot study data stored in {pharmaversesdtm}.

# Load Packages
library(admiral)
library(dplyr)
library(lubridate)
library(stringr)
library(metacore)
library(metatools)
library(xportr)
library(readr)
library(pharmaversesdtm)
library(pharmaverseadam)

Next Load Specifications for Metacore

We have saved our specifications in an Excel file and will load them into {metacore} with the metacore::spec_to_metacore() function.

# ---- Load Specs for Metacore ----
metacore <- spec_to_metacore("./metadata/pk_spec.xlsx") %>%
  select_dataset("ADPPK")

Load Source Datasets

We will load are SDTM data from {pharmaversesdtm}. The main components of this will be exposure data from EX and pharmacokinetic concentration data from PC. We will use ADSL for baseline characteristics and we will derive additional baselines from vital signs VS and laboratory data LB.

# ---- Load source datasets ----
# Load PC, EX, VS, LB and ADSL
data("pc")
data("ex")
data("vs")
data("lb")

data("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)

Derivations

Derive PC Dates

Here we use {admiral} functions for working with dates and we will also create a nominal time from first dose NFRLT for PC data based on PCTPTNUM.

# ---- Derivations ----

# Get list of ADSL vars required for derivations
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 event ID and nominal relative time from first dose (NFRLT)
  mutate(
    EVID = 0,
    DRUG = PCTEST,
    NFRLT = if_else(PCTPTNUM < 0, 0, PCTPTNUM), .after = USUBJID
  )

Get Dosing Information

Here we also create nominal time from first dose NFRLT for EX data based on VISITDY.

# ---- 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))

Expand Dosing Records

Since there is a start date and end date for dosing records we need to expand the dosing records between the start date and end date using the function admiral::create_single_dose_dataset().

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))

Find First Dose

In this section we will find the first dose for each subject and drug.

# ---- Find first dose per treatment per subject ----
# ---- Join with ADPPK 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),
  )

Find Previous Dose

For ADPPK we will find the previous dose with respect to actual time and nominal time.

# ---- 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"
  )

Combine PC and EX Data

Here we 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).

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
    )
  )

Derive Analysis Variables

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,
    DVID = PCTESTCD,
    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
    ),
    RLTU = "h",
    USTRESC = PCSTRESC,
    UDTC = format_ISO8601(ADTM),
    II = if_else(EVID == 1, 1, 0),
    SS = if_else(EVID == 1, 1, 0),
    ADDL = 0,
    OCC = 1,
  )

Add ASEQ

# ---- 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"
  ) %>%
  mutate(
    PROJID = DRUG,
    PROJIDN = 1,
    PART = 1,
  )

Derive Covariates Using {metatools}

In this step we will create our numeric covariates using the metatools::create_var_from_codelist() function.

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

covar <- adsl %>%
  create_var_from_codelist(metacore, input_var = STUDYID, out_var = STUDYIDN) %>%
  create_var_from_codelist(metacore, input_var = SEX, out_var = SEXN) %>%
  create_var_from_codelist(metacore, input_var = RACE, out_var = RACEN) %>%
  create_var_from_codelist(metacore, input_var = ETHNIC, out_var = AETHNIC) %>%
  create_var_from_codelist(metacore, input_var = AETHNIC, out_var = AETHNICN) %>%
  create_var_from_codelist(metacore, input_var = ARMCD, out_var = COHORT) %>%
  create_var_from_codelist(metacore, input_var = ARMCD, out_var = COHORTC) %>%
  create_var_from_codelist(metacore, input_var = COUNTRY, out_var = COUNTRYN) %>%
  create_var_from_codelist(metacore, input_var = COUNTRY, out_var = COUNTRYL) %>%
  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),
    ROUTE = unique(ex$EXROUTE),
    FORM = unique(ex$EXDOSFRM),
    REGION1 = COUNTRY,
    REGION1N = COUNTRYN,
    SUBJTYPC = "Volunteer",
  ) %>%
  create_var_from_codelist(metacore, input_var = FORM, out_var = FORMN) %>%
  create_var_from_codelist(metacore, input_var = ROUTE, out_var = ROUTEN) %>%
  create_var_from_codelist(metacore, input_var = SUBJTYPC, out_var = SUBJTYP)

Derive Additional Baselines

Next we add additional baselines from vital signs and laboratory data.

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"
    ),
    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)

Combine with Covariates

We combine our covariates with the rest of the data

# Combine covariates with APPPK data

adppk_prefinal <- adppk_aseq %>%
  derive_vars_merged(
    dataset_add = select(covar_vslb, !!!negate_vars(adsl_vars)),
    by_vars = exprs(STUDYID, USUBJID)
  ) %>%
  arrange(STUDYIDN, USUBJIDN, AFRLT, EVID) %>%
  # Add RECSEQ
  # Exclude records if needed
  mutate(
    RECSEQ = row_number(),
    EXCLFCOM = "None"
  ) %>%
  create_var_from_codelist(metacore, input_var = DVID, out_var = DVIDN) %>%
  create_var_from_codelist(metacore, input_var = EXCLFCOM, out_var = EXCLF)

Check Data With metacore and metatools

We use {metacore} objects with {metatools} functions to perform a number of checks on the data. We will drop variables not in the specs and make sure all the variables from the specs are included.

adppk <- adppk_prefinal %>%
  drop_unspec_vars(metacore) %>% # Drop unspecified variables from specs
  check_variables(metacore) %>% # Check all variables specified are present and no more
  check_ct_data(metacore) %>% # Checks all variables with CT only contain values within the CT
  order_cols(metacore) %>% # Orders the columns according to the spec
  sort_by_key(metacore) # Sorts the rows by the sort keys

Apply Labels and Formats with xportr

Using {xportr} we check variable type, assign variable lenght, add variable labels, add variable formats, and save a transport file with xportr::xportr_write().

dir <- tempdir() # Change to whichever directory you want to save the dataset in

adppk_xpt <- adppk %>%
  xportr_type(metacore, domain = "ADPPK") %>% # Coerce variable type to match spec
  xportr_length(metacore) %>% # Assigns SAS length from a variable level metadata
  xportr_label(metacore) %>% # Assigns variable label from metacore specifications
  xportr_format(metacore) %>% # Assigns variable format from metacore specifications
  xportr_df_label(metacore) %>% # Assigns dataset label from metacore specifications
  xportr_write(file.path(dir, "adppk.xpt")) # Write xpt v5 transport file