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

ABSTRACT:

Background

The 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.

Methods

The 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 Baye

SUBMITTER: Chiaruttini MV 

PROVIDER: S-EPMC11924708 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature