gammaFuncModel (version 5.0)

pk_calculation: Function that returns a data frame for Tmax, Cmax, half-life, AUC and AUCInf for metabolites

Description

Function that returns a data frame for Tmax, Cmax, half-life, AUC and AUCInf for metabolites

Usage

pk_calculation(df, met_vec, models, grp_name = "Diet", covariates, ref = 1)

Value

Data frame with the pharmacokinetic properties of each metabolite

Arguments

df

Data frame containing columns Group(factor); ID(subject ID: character); Time(positive: numeric); other individiual characteristics covariates (exlcluding other forms of 'Time') Note: Data must be complete (No missing values).

met_vec

Vector of metabolite names

models

Fitted models for all metabolites of interest

grp_name

Name of the grouping variable

covariates

Vector containing the names of the "ID" covariate, grouping covariate and other covariates excluding any "Time" covariates

ref

reference level for the grouping variable. could be numeric or character

References

Wickham, H. (2022). dplyr: A Grammar of Data Manipulation. R package version 1.0.10. Available at: https://CRAN.R-project.org/package=dplyr

Pinheiro, J. C., & Bates, D. M. (2022). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-153. Available at: https://CRAN.R-project.org/package=nlme

Examples

require(gammaFuncModel)
require(dplyr)
# \donttest{
df <- data.frame(
 ID = rep(sprintf("%02d", 1:10), each = 9 * 3),             
 Time = rep(rep(1:9, each = 3), 10),                      
 Diet = as.factor(rep(1:3, times = 9 * 10)),             
 Age = rep(sample(20:70, 10, replace = TRUE), each = 9 * 3), 
 BMI = round(rep(runif(10, 18.5, 35), each = 9 * 3), 1)     
)
metvar <- paste0("met", 1:10)
n_rows <- nrow(df)
concentration_data <- sapply(1:10, function(m) {
 shape <- runif(1, 2, 5)     
 scale <- runif(1, 1, 3)     
 rgamma(n_rows, shape = shape, scale = scale)
})
colnames(concentration_data) <- metvar
df <- cbind(df, as.data.frame(concentration_data))
covariates <- c("ID", "Diet", "Age", "BMI")
mods <- generate_models(df = df, met_vec = metvar, covariates = covariates, graph = 'None')
result <- pk_calculation(
  df = df, 
  met_vec = metvar, 
  models = mods, 
  grp_name = "Diet", 
  covariates = covariates
 )
# }