Bayesian Power Prior in Platform Trials With Non-Concurrent Control for Binary Outcomes: Development and Comparative...

Platform trials enable the evaluation of multiple investigational drugs for a single disease and offer flexibility in adding or dropping treatments during the trial. This design would be advantageous for reducing the sample size and drug development time, particularly in contexts such as pandemics. In the platform trials, non-concurrent controls (NCCs) are often used for drug-control comparisons, but temporal shifts in subject characteristics, trial conduct, or standard of care can introduce bias in the estimation of treatment effects and increase the type I error rate. In this study, we develop a new Bayesian power prior to incorporate NCC data in platform trials with binary outcomes. To address temporal shifts, our method adjusts the amount of information borrowed from NCCs using a data-driven similarity index between NCC and concurrent control (CC) data. This index serves as the power parameter in the power prior, enabling adaptive borrowing. We evaluated the proposed method through extensive simulation studies, comparing its operating characteristics with seven alternatives: analysis using only CC data, naïve pooling method, a frequentist linear regression model, and four Bayesian methods designed to address temporal shifts. Across a range of temporal shift scenarios, the proposed method consistently achieved a favorable balance between type I error control and statistical power, maintaining type I error rates below 10% while avoiding the overborrowing seen in more aggressive methods. The practical utility of the proposed method was also examined by applying it to data from a platform trial involving patients with COVID-19.