Identifying Treatment Effect Heterogeneity with Bayesian Hierarchical Adjustable Random Partition in Adaptive Enrichment Trials

2025-08-22

AI Summary

This study addresses the identification of treatment effect heterogeneity and estimation of subgroup-specific effects in clinical trials. Conventional partitioning methods suffer from limited flexibility, fixed information borrowing strength across subgroups, and difficulty handling model uncertainty. To overcome these limitations, we propose the Bayesian Hierarchical Adaptive Random Partitioning (BHARP) model: it employs a finite mixture framework with an unknown number of components and leverages reversible-jump MCMC for automatic subgroup discovery; hierarchical priors adaptively modulate within-cluster information borrowing strength, enabling data-driven, robust posterior inference without auxiliary analyses. Across diverse heterogeneity scenarios—including qualitative and quantitative effect modification—BHARP achieves substantially improved estimation accuracy and precision compared to state-of-the-art methods. Its practical utility is demonstrated through application to a multi-arm adaptive enrichment trial in diabetes.

Technology Category

• Machine Learning: Bayesian Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Treatment effect heterogeneity refers to the systematic variation in treatment effects across subgroups. There is an increasing need for clinical trials that aim to investigate treatment effect heterogeneity and estimate subgroup-specific responses. While several statistical methods have been proposed to address this problem, existing partitioning-based methods often depend on auxiliary analysis, overlook model uncertainty, or impose inflexible borrowing strength. We propose the Bayesian Hierarchical Adjustable Random Partition (BHARP) model, a self-contained framework that applies a finite mixture model with an unknown number of components to explore the partition space accounting for model uncertainty. The BHARP model jointly estimates subgroup-specific effects and the heterogeneity patterns, and adjusts the borrowing strengths based on within-cluster cohesion without requiring manual calibration. Posterior sampling is performed via a custom reversible-jump Markov chain Monte Carlo sampler tailored to partitioning-based information borrowing in clinical trials. Simulation studies across a range of treatment effect heterogeneity patterns show that the BHARP model achieves better accuracy and precision compared to conventional and advanced methods. We showcase the utilities of the BHARP model in the context of a multi-arm adaptive enrichment trial investigating physical activity interventions in patients with type 2 diabetes.

Problem

• Identifying treatment effect heterogeneity across subgroups
• Estimating subgroup-specific treatment responses accurately
• Overcoming limitations of existing partitioning-based statistical methods

Innovation

• Bayesian Hierarchical Adjustable Random Partition model
• Finite mixture model with unknown components
• Reversible-jump Markov chain Monte Carlo sampler