Adaptive Optimal Design for Bridging Studies with an Application to Population Pharmacokinetic Studies

Abstract

To develop and evaluate methods for conducting adaptive population pharmacokinetic bridging studies. An adaptive D-optimal design based on optimization of the population Fisher information matrix was used to determine the best sampling schedule for a target-population. Recruitment of the target-population was divided into batches and patients are assumed to enroll by batch. A prior-population model was used to determine the optimal sampling schedule for the first batch and to stabilise the data analysis in the interim iteration. Simulation studies were performed under two scenarios (1) the prior- and target-populations have similar pharmacokinetic profiles and (2) the pharmacokinetic profiles diverge significantly. A design criterion to determine early full enrollment was also proposed. The target-population estimates obtained using the proposed method were compared to estimates obtained if the target-population was studied with a design optimized based on the prior-population model. The proposed method is shown to be not inferior in scenario (1) and superior in scenario (2). The criterion to determine early full enrollment was proven to be effective. An adaptive optimal design method together with an early full enrollment criterion were evaluated and resulted in more accurate estimates for the target-population in bridging studies.