PK Examples

Pharmaverse PK Examples

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One of these is the ability to launch Posit Cloud to explore the example code and make your own modifications. This interactive Posit Cloud environment is preconfigured with all required package installations. Click here: Launch Posit Cloud to explore the examples code.

This sample code here is based on the Population PK Analysis Data (ADPPK) model which follows the recently released CDISC Implementation Guide.

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. 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} and the ADaM ADSL data will come from {pharmaverseadam}.

# 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("pk_spec.xlsx") %>%
  select_dataset("ADPPK")

Load Source Datasets

We will load our SDTM data from {pharmaversesdtm}. The main components of the Population PK will be exposure data from EX and pharmacokinetic concentration data from PC. Here we will use ADSL from {pharmaverseadam} 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)

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. Several common variables are computed using some of the built in functions in {admiral}.

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)

This covariate section of the code will be combined with the dosing and observation records from EX and PC.

The rest of the code can be seen on the pharmaverse examples website or in the Posit Cloud environment.

Happy exploring!

Last updated

2024-05-08 15:37:35.680799

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