A Bayesian predictive framework for adaptive interim-analysis timing with robust borrowing in confirmatory trials

2026-07-05

AI Summary

This study addresses a key challenge in confirmatory Phase III trials: how to appropriately leverage Phase II data and dynamically determine the timing of interim analyses when there is limited evidence from agents with similar mechanisms, all while controlling the overall Type I error rate. The authors propose the B²-FIC framework, which for the first time dynamically links interim analysis timing to the strength of evidence derived from Bayesian information borrowing. By integrating Phase II data with early Phase III observations to update posterior distributions, the method employs Bayesian predictive probabilities to define adaptive rules that select the earliest feasible second interim analysis timepoint meeting predefined efficacy thresholds. Coupled with group sequential design calibration, B²-FIC maintains strict Type I error control while substantially enhancing statistical power; simulations demonstrate that under favorable early evidence, it triggers interim analyses earlier than conventional designs, thereby improving trial efficiency.

Abstract

Confirmatory phase III trials require rigorous evidence, yet for first-in-class (FIC) therapies they must often be designed when same-mechanism evidence is scarce. This uncertainty motivates planned interim analyses and makes phase II data from the same therapy a relevant source of prior evidence. However, both borrowing and repeated interim analyses must be calibrated to control the overall type I error rate. Because borrowing changes the evidence available at interim analyses relative to a non-borrowing group sequential design (GSD), it also raises the question of whether interim analysis timing should be prospectively adapted to the borrowing-adjusted evidence base. We propose a prespecified adaptive interim-timing framework based on Bayesian information borrowing and Bayesian predictive probability $B^2$-FIC. The borrowing model is calibrated against phase II--phase III discrepancy scenarios to control overall type I error rate. At the first interim analysis (IA1), the calibrated model combines phase II information with accumulating phase III data to update the posterior. Bayesian predictive probabilities from this posterior select the earliest information fraction for the second interim analysis (IA2) that meets the efficacy criterion. In simulations, $B^2$-FIC maintained empirical type I error and improved interim power across different scenarios. Predictive probabilities derived from phase II and phase III IA1 data selected earlier IA2 than GSD when evidence was favorable. Two oncology case studies illustrate the framework. Overall, $B^2$-FIC provides a calibrated framework for adapting interim timing to borrowing-adjusted evidence, an emerging design problem in confirmatory trials.

Problem

• confirmatory trials
• interim analysis timing
• evidence borrowing
• type I error control
• first-in-class therapies

Innovation

• Bayesian borrowing
• adaptive interim analysis
• type I error control
• predictive probability
• group sequential design