Podcast Episode
52: Bayesian Borrowing in Phase 3 Trials
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Scott Berry, Ph.D.
President & Senior Statistical Scientist
Kert Viele, Ph.D
Director & Senior Statistical Scientist
In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele examine Bayesian borrowing in Phase 3 clinical trials, focusing on statistical handling of prior information and real-world FDA interactions.
In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele examine Bayesian borrowing in Phase 3 clinical trials, focusing on statistical handling of prior information and real-world FDA interactions. The episode opens with an analogy, comparing prior probability in Bayesian analysis to interpreting a home pregnancy test, succinctly demonstrating the effect of prior knowledge on trial interpretation. The discussion addresses technical challenges—how borrowing inflates Type I errors and why this is addressed differently under Bayesian operating characteristics. Concrete examples include dynamic versus static borrowing approaches, and formal integration of prior evidence in regulatory submissions. Case studies center on the WATCHMAN device (PROTECT AF and PREVAIL trials) and REBYOTA, illustrating FDA engagement, relevant trial design tactics, and published outcomes. The episode also critiques common pitfalls such as selective data use and improper prior construction, emphasizing the FDA’s focus on comprehensive and unbiased historical sources.
Key Highlights
Pregnancy test analogy used to clarify prior probability in trial interpretation.
Bayesian borrowing’s effects on Type I error and statistical thresholds.
Case studies: WATCHMAN device (PROTECT AF, PREVAIL) and REBYOTA approvals.
Dynamic borrowing versus static borrowing strategies in regulatory settings.
Risks of cherry-picking and importance of unbiased, relevant prior data.
FDA guidance and review procedures for Bayesian trials.
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52: Bayesian Borrowing in Phase 3 Trials
In this episode of "In the Interim…", Dr. Scott Berry and Dr. Kert Viele examine Bayesian borrowing in Phase 3 clinical trials, focusing on statistical handling of prior information and real-world FDA interactions.
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