A Bayesian Design For Platform Trials With Temporal Changes

Abstract

The platform trial, which aims to find the best treatment for a disease by sequentially investigating multiple treatments in a single trial, has become increasingly popular in recent decades. An inherent problem for a platform trial is how to borrow information from the non-current controls to improve the efficiency of the statistical inference. The practical solution of directly combining all the control patients does not work due to the population heterogeneity between the concurrent and non-current controls. The temporal changes are the significant resources for that heterogeneity, which will affect patients’ responses over time. In this paper, we develop a Bayesian design to evaluate treatment effects of platform trials accounting for temporal changes. We treat each cohort of patients as a matching set and develop a conditional likelihood method to eliminate the impact of temporal changes. The performance of the proposed method is evaluated through simulation studies.