A Bayesian Adaptive Design for Assessing Treatment Effect Consistency in Bridging Studies.

Bridging studies serve as an efficient approach to evaluate the applicability of findings from an original study to a new population. We propose a Bayesian adaptive design for bridging studies with time-to-event endpoints, aiming to assess the consistency of treatment effects with those observed in the original study. The design adopts a group sequential framework, using posterior probabilities at interim and final analyses to evaluate the evidence for consistency between the bridging and original studies. If strong evidence of consistency or inconsistency is observed, the trial may be stopped early to conserve sample size. Additionally, a sample size recalculation strategy based on Bayesian predictive probability is incorporated to enhance flexibility. Simulation studies demonstrate that, compared to a commonly used method, the proposed design offers favorable operating characteristics. It maintains control of the empirical type I error rate through prespecified calibration, accounting for interim analyses (IAs) and sample size adaptation, and yields substantial sample size savings when the treatment effect in the bridging study is either highly consistent or clearly inconsistent with that of the original study.