Interoccasion variability in population pharmacokinetic models: identifiability, influence, interdependencies and derived study design recommendations.

Modeling interoccasion variability (IOV) of pharmacokinetic parameters is challenging in sparse study designs. We conducted a simulation study with stochastic simulation and estimation (SSE) to evaluate the influence of IOV (25, 75%CV) from numerous perspectives (power, type I error, accuracy and precision of parameter estimates, consequences of neglecting an IOV, capability to detect the 'correct' IOV). To expand the scope from modeling-related aspects to clinical trial practice, we investigated the minimal sample size for IOV detection and calculated areas under the concentration-time curve (AUC) derived from models containing IOV and mis-specified models. The power to correctly detect an IOV increased from one to three occasions (OCC) and the type I error rate to falsely include an IOV was not elevated. Two sampling schemes were compared (with/without trough sample) and including a trough sample resulted in better performance throughout the different evaluations in this simulation study. Parameters were estimated more precisely when more OCCs were included and IOV was of high effect size. Neglecting an IOV that was truly present had a high impact on bias and imprecision of the parameter estimates, mostly on interindividual variabilities and residual error. To reach a power of ≥ 95% in all scenarios when sampling in three OCCs between 10 and 50 patients were required in the investigated setting. AUC calculations with mis-specified models revealed a distorted AUC distribution as IOV was not considered.