poso_estim_map function

Estimate the Maximum A Posteriori individual parameters

Estimates the Maximum A Posteriori (MAP) individual parameters, also known as Empirical Bayes Estimates (EBE).

poso_estim_map(
  dat = NULL,
  prior_model = NULL,
  return_model = TRUE,
  return_ofv = FALSE,
  nocb = FALSE
)

Arguments

• dat: Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
• prior_model: A posologyr prior population pharmacokinetics model, a list of six objects.
• return_model: A boolean. Returns a rxode2 model using the estimated ETAs if set to TRUE.
• return_ofv: A boolean. Returns a the Objective Function Value (OFV) if set to TRUE.
• nocb: A boolean. for time-varying covariates: the next observation carried backward (nocb) interpolation style, similar to NONMEM. If FALSE, the last observation carried forward (locf) style will be used. Defaults to FALSE.

Returns

A named list consisting of one or more of the following elements depending on the input parameters of the function: $eta a named vector of the MAP estimates of the individual values of ETA, $model an rxode2 model using the estimated ETAs, $event the data.table used to solve the returned rxode2 model.

Examples

rxode2::setRxThreads(1) # limit the number of threads

# model
mod_run001 <- function() {
  ini({
    THETA_Cl <- 4.0
    THETA_Vc <- 70.0
    THETA_Ka <- 1.0
    ETA_Cl ~ 0.2
    ETA_Vc ~ 0.2
    ETA_Ka ~ 0.2
    prop.sd <- sqrt(0.05)
  })
  model({
    TVCl <- THETA_Cl
    TVVc <- THETA_Vc
    TVKa <- THETA_Ka

    Cl <- TVCl*exp(ETA_Cl)
    Vc <- TVVc*exp(ETA_Vc)
    Ka <- TVKa*exp(ETA_Ka)

    K20 <- Cl/Vc
    Cc <- centr/Vc

    d/dt(depot) = -Ka*depot
    d/dt(centr) = Ka*depot - K20*centr
    Cc ~ prop(prop.sd)
  })
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
                        TIME=c(0.0,1.0,14.0),
                        DV=c(NA,25.0,5.5),
                        AMT=c(2000,0,0),
                        EVID=c(1,0,0),
                        DUR=c(0.5,NA,NA))
# estimate the Maximum A Posteriori individual parameters
poso_estim_map(dat=df_patient01,prior_model=mod_run001)