% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{ds_covval}
\alias{ds_covval}
\title{Warfarin covariate values dataset}
\format{
\subsection{\code{ds_covval}}{

A data frame with 100 rows and 9 columns:
\describe{
\item{ID}{individual patient identifier}
\item{AGE}{age in years}
\item{WEIGHT}{body weight in kg}
\item{BMI}{body mass index (kg/m\ifelse{html}{\out{<sup>2</sup>}}{\eqn{^2}})}
\item{LG_WEIGHT}{log-transformed body weight (log10 scale), used as continuous covariate in the PK model}
\item{LG_AGE}{log-transformed age (log10 scale), used as continuous covariate in the PK model}
\item{SEX}{sex of the individual (0 = female, 1 = male)}
\item{CYP2C9}{CYP2C9 genotype encoded as integer: 0 = \emph{*1/*1} (reference), 1 = \emph{*1/*2}, 2 = \emph{*1/*3}, 3 = \emph{*2/*2}, 4 = \emph{*2/*3}, 5 = \emph{*3/*3}}
\item{VKORC1}{VKORC1 genotype (\code{"GG"}, \code{"AG"}, or \code{"AA"})}
}
}
}
\source{
Derived from \code{data_fin_i.csv} — individual covariate dataset for the warfarin PopPK covariate sensitivity analysis.
}
\usage{
ds_covval
}
\description{
Individual covariate values for 100 patients used in the warfarin
covariate sensitivity simulation, including demographic and pharmacogenomic variables.
}
\keyword{datasets}
