Bayesian Design and Analysis of Precision Trials with Partial Borrowing

2026-03-11

AI Summary

This study addresses the challenge of reliably estimating treatment effects and interactions in precision medicine clinical trials, where target subgroups often suffer from sparse sample sizes. To overcome this limitation, the authors propose a Bayesian framework that partially borrows information from external data sources—such as retrospective studies or early-phase trials—during both trial design and analysis. The approach assigns fitness-based weights to individual external observations through covariate distribution matching, enabling precise information borrowing. Innovatively integrating covariate matching, individual-level weighting, and Bayesian modeling, the method also incorporates design priors to determine sample size and decision boundaries. Simulation studies demonstrate its superior performance over existing dynamic borrowing strategies across diverse scenarios, yielding substantially improved accuracy in subgroup effect estimation. The framework is successfully illustrated through an application to a gastric cancer clinical trial design.

Technology Category

• Machine Learning: Bayesian Learning
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

With the advancement of precision medicine there is an increasing need for design and analysis methods in clinical trials with the objective of investigating effect heterogeneity and estimating subgroup effects. As this requires precise estimation of interaction effects, borrowing information from external data sources including retrospective studies and early phase clinical trials to enrich the trial in sparse subgroups is pertinent. Motivated by a trial in gastric cancer we consider a practical design and analysis framework for borrowing from external data sources that only partially inform the inference. As the analysis model we propose an individually weighted model where the external data are weighted based on their fit with the target population based on the distribution of a set of covariates. In a simulation study we assess the performance of the model under various scenarios and make comparisons to dynamic borrowing. In addition, we provide a Bayesian design framework where design priors are extracted from the external data to determine decision boundaries and sample sizes. The design procedure is demonstrated within the context of our motivating example.

Problem

• precision medicine
• effect heterogeneity
• subgroup analysis
• external data borrowing
• clinical trial design

Innovation

• Bayesian design
• partial borrowing
• individually weighted model
• precision trials
• external data integration