Sample size reassessment in Bayesian hybrid clinical trials

2025-12-22

AI Summary

Historical control data in Bayesian hybrid clinical trials often introduce bias due to shifts in patient populations, diagnostic criteria, or treatment standards. Method: We propose a two-arm randomized design featuring an interim analysis that first quantifies similarity between historical and concurrent controls using the Hellinger distance; this metric dynamically calibrates the prior variance for the control arm and re-estimates the sample size for Stage 2. The method is unified for both continuous and binary endpoints. Contribution/Results: It jointly optimizes prior robustness and sample-size adaptability. Simulation studies demonstrate substantially improved bias control under varying historical–control heterogeneity, while maintaining statistical power and design robustness. The framework provides a generalizable, adaptive solution for hybrid trial designs.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The use of historical controls offers a valuable alternative when traditional randomized controlled trials are not feasible. However, such approaches may introduce bias due to temporal changes in patient populations, diagnostic criteria, and/or treatment standards. Hybrid designs, which combine a concurrent control arm with historical control data, can help mitigate the possible bias. We propose a novel Bayesian two-arm randomized clinical trial design incorporating an interim analysis. At the interim analysis, a new criterion derived from the Hellinger distance is used to quantify the similarity between historical and concurrent control data outcomes. This measure informs both (1) the variance function of the control prior distribution in the final analysis and (2) the sample size reassessment for the second stage of the trial. The proposed approach is designed to accommodate both continuous and binary endpoints and is assessed through extensive simulation studies. Results demonstrate the method flexibility and robustness in adapting to varying degrees of historical-control heterogeneity.

Problem

• Proposes Bayesian hybrid trial design with interim analysis
• Uses Hellinger distance to assess historical-concurrent control similarity
• Adapts prior variance and sample size based on similarity measure

Innovation

• Bayesian hybrid design with interim analysis for bias mitigation
• Hellinger distance measures historical-concurrent control similarity
• Dynamic sample size reassessment based on control data variance