A Bayesian adaptive enrichment design using aggregate historical data to inform individualized treatment recommendations

2026-03-10

AI Summary

This study addresses the challenge in adaptive enrichment trials where only aggregated historical data—such as average treatment effects—are available, rendering individualized treatment effects difficult to infer, particularly when biomarker subgroup parameters are non-identifiable. To overcome this limitation, the authors propose a Bayesian adaptive enrichment design that, for the first time, enables identifiable inference on subgroup-specific treatment effects using only summary-level historical evidence. The approach robustly integrates external data via a normalized power prior and dynamically guides biomarker-based participant recruitment and personalized treatment recommendations. By incorporating interim analyses and posterior probability–based decision rules, the method substantially improves statistical power, shortens trial duration, and reduces expected sample size, as demonstrated in simulations within the context of obstructive sleep apnea.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Bayesian Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Adaptive enrichment trials aim to identify and recruit participants most likely to benefit from treatment based on evolving biomarker evidence, with the goal of informing individualized treatment recommendations. Bayesian methods are well suited to these designs because they allow external information to be incorporated in a principled manner. In practice, prior studies often provide only summary-level information, with subgroup-specific estimates unavailable due to design or privacy constraints. Existing dynamic borrowing approaches therefore rely on aggregate measures, such as the average treatment effect, and implicitly assume that historical information maps directly onto model parameters. In adaptive enrichment settings aimed at identifying individualized treatment effects, however, subgroup-specific treatment parameters are not identifiable when only marginal historical effects are available. To address this gap, we propose a Bayesian adaptive enrichment design that borrows information from external studies using a normalized power prior anchored on one or more summary measures, such as the average treatment effect. Interim analyses use posterior probabilities to guide early stopping for efficacy or futility, or to continue recruitment within promising biomarker-defined subgroups. Simulation studies evaluate operating characteristics across historical bias, sample size, and prior informativeness. Together with a motivating future trial in obstructive sleep apnea, the results show efficiency gains versus non-borrowing designs, including improved power, earlier stopping, and reduced expected sample size.

Problem

• adaptive enrichment
• Bayesian design
• historical data
• individualized treatment
• subgroup analysis

Innovation

• Bayesian adaptive enrichment
• normalized power prior
• historical data borrowing
• individualized treatment recommendation
• subgroup analysis