Covariates are implemented using the new_covariate()
function, wrapped in a
named list. For example:
library(PKPDsim)
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-varying covariates, such as creatinine values can be implemented easily as well. They just require the additional times
argument:
covariates <- list( "WT" = new_covariate(value = 70), "CR" = new_covariate( value = c(0.8, 1, 1.2), times = c(0, 48, 72) ) )
By default, PKPDsim
assumes that you want to interpolate (linearly) 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 new_covariate()
.
A table of covariates can be supplied to sim()
with covariate values per
individual. It can handle both static and time-varying covariates. A covariate
table could look like this:
cov_table <- data.frame( id = c(1, 1, 2, 3), WT = c(40, 45, 50, 60), SCR = c(50, 150, 90, 110), t = c(0, 5, 0, 0) )
The id
and t
(time) columns can be omitted when only static covariates are
to be used. Again, make sure that the headers used for the covariates match
exactly with the covariate names specified in the model definition.
A full example for a model with (simulated) covariates for a patient population:
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[1] = -KA*A[1] dAdt[2] = KA*A[1] -(CLi/Vi)*A[2] ', 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', interval = 12 ) dat <- sim( ode = model, parameters = 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: 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.
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