Bayesian dynamic borrowing in group-sequential design for medical device studies.

backgroundThe integration of historical data into ongoing clinical trials through Bayesian Dynamic Borrowing offers significant advantages, including reduced sample size, trial duration, and associated costs. However, challenges such as ensuring exchangeability between historical and current data and mitigating Type I error inflation remain critical. This study proposes a Bayesian group-sequential design incorporating a Self-Adaptive Mixture (SAM) prior framework to address these challenges in medical device trials.

methodsThe SAM prior combines informative priors derived from historical data with weakly informative priors, dynamically adjusting the weight of historical information based on congruence with current trial data. The design includes interim analyses, with Bayesian decision rules leveraging futility and efficacy boundaries derived using the frequentist spending functions. Effective Sample Size calculations informed adjustments to sample size and allocation ratios between experimental and control arms at each interim. The methodology was evaluated using a motivating example from a cardiovascular device trial with a noninferiority hypothesis.

resultsFour historical studies with substantial heterogeneity were incorporated. The SAM prior showed improved adaptation to prior-data conflicts compared to static methods, maintaining Type I error and Power at their nominal levels. In the motivating trial, the MAP prior was approximated as a mixture of beta distributions, facilitating congruence testing and posterior inference. Simulation studies confirmed the proposed design's efficiency under both congruent and incongruent scenarios.

conclusionsThe proposed Bayesian Group-Sequential Design with SAM prior offers a robust, adaptive framework for medical device trials, balancing statistical rigor with clinical interpretability. This approach enhances decision-making and supports timely, cost-effective evaluations, particularly in dynamic contexts like medical device development.