Covariates are implemented using the
new_covariate() function, wrapped in a
named list. E.g. the following is the most basic specification:
dat <- sim_ode( ode = model, parameters = parameters, regimen = regimen, covariates = list("WT" = new_covariate(value = 70), "SCR" = new_covariate(value = 120)) )
The names in the covariate list-object should correspond exactly with the names of the covariates in the model.
Time-varing covariates, such as creatinine values can be implemented easily as well. They just require the additional
dat <- sim_ode( ode = model, parameters = parameters, regimen = regimen, covariates = list("WT" = new_covariate(value = 70), "CR" = new_covariate( value = c(0.8, 1, 1.2), times = c(0, 48, 72)) ) )
PKPDsim assumes that you want to interpolate between measurements of the time-varying covariates. If you prefer to implement the covariate using last-observation-carried-forward (in other words a step function), specify the
method = "LOCF" argument to the
Covariates for multiple patients
A table of covariates can be supplied to
sim_ode() with covariate values per individual. It can handle both static and time-varying covariates. A covariate table could look like this:
id WT SCR t 1 40 50 0 1 45 150 5 2 50 90 0 3 60 110 0
t (time) columns can be omitted when only static covariates are to be used. Make sure that the headers used for the covariates match exactly with the covariate names specified in the model definition.
With the above
cov_table, the call to
sim_ode() would then become:
sim_ode(ode = model, parameters = parameters, regimen = regimen, covariates_table = cov_table)
A full example for a model with (simulated) covariates for a patient population would be:
library(PKPDsim) parameters <- list(CL = 1, V = 10, KA = 0.5) n_ind <- 50 cov_table <- data.frame('id' = 1:n_ind, 'WT' = rnorm(n_ind, mean = 70, sd = 5)) model <- new_ode_model( code = ' CLi = CL * pow((WT/70), 0.75) Vi = V * (WT/70) dAdt = -KA*A dAdt = KA*A -(CLi/Vi)*A ', declare_variables = c('CLi', 'Vi'), covariates = c('WT'), dose = list(cmt = 1), obs = list(cmt = 2, scale = 'V * (WT/70)') ) regimen <- new_regimen(amt = 30, n = 4, type = 'bolus') dat <- sim(ode = 'model', par = parameters, t_obs = c(0.5, 2, 4, 8, 12, 16, 24), n_ind = n_ind, regimen = regimen, covariates_table = cov_table, output_include = list(covariates=TRUE))
Note: at current, PKPDsim does not handle missing covariate values. If you do have missing covariate data, probably the best approach would be to impute the values manually before simulation, e.g. based on the mean observed / known value, or the correlation between the covariates.