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.

翻译：治疗效应异质性指治疗效果在不同亚组间存在的系统性差异。当前对旨在探究治疗效应异质性并估计亚组特异性反应的临床试验需求日益增长。尽管已有多种统计方法被提出以解决此问题，但现有的基于划分的方法往往依赖于辅助分析、忽略模型不确定性或施加了不灵活的借力机制。我们提出贝叶斯分层可调随机划分模型，这是一个自包含的框架，通过应用具有未知组分数量的有限混合模型来探索划分空间，同时考虑模型不确定性。该模型联合估计亚组特异性效应与异质性模式，并基于簇内凝聚性调整借力强度，无需人工校准。后验抽样通过定制的可逆跳转马尔可夫链蒙特卡洛采样器实现，该采样器专为临床试验中基于划分的信息借力量身打造。在一系列治疗效应异质性模式下的模拟研究表明，与传统及先进方法相比，该模型实现了更高的准确性与精确度。我们在针对2型糖尿病患者体力活动干预的多臂自适应富集试验中展示了该模型的实用性。