Pharmacokinetic (PK) analysis data programming poses some unique challenges.  For example, both dosing and concentration records are included, and nominal and actual relative time variables that reference the first dose, the previous dose, or the next upcoming dose are required.  Modelling data may require a number of numeric covariates.  CDISC ADaM submission standards for Non-compartmental Analysis (ADNCA) data have recently been published, and new standards for Population PK (ADPPK) are forthcoming.  {admiral} is an open-source R package for creating CDISC ADaM data.  It can be used effectively to create both types of PK analysis data.  Additional tools from other Pharmaverse packages such as {metacore} and {metatools} can be used to simplify the workflow.  I will discuss some of the challenges of Pharmacokinetic data programming and show some of the solutions developed in {admiral} and the Pharmaverse.
Jeff Dickinson
Navitas Data Sciences
Pharmacokinetic (PK) data programming in {admiral} and the Pharmaverse
Pharmacokinetic (PK) analysis data programming poses some unique challenges.  For example, both dosing and concentration records are included, and nominal and actual relative time variables that reference the first dose, the previous dose, or the next upcoming dose are required.  Modelling data may require a number of numeric covariates.  CDISC ADaM submission standards for Non-compartmental Analysis (ADNCA) data have recently been published, and new standards for Population PK (ADPPK) are forthcoming.   admiral  is an open-source R package for creating CDISC ADaM data.  It can be used effectively to create both types of PK analysis data.  Additional tools from other Pharmaverse packages such as  metacore  and  metatools  can be used to simplify the workflow.  I will discuss some of the challenges of Pharmacokinetic data programming and show some of the solutions developed in  admiral  and the Pharmaverse.