Precision medicine is an emerging field that takes into account individual heterogeneity to inform better clinical practice. In clinical trials, the evaluation of treatment effect heterogeneity is an important component, and recently, many statistical methods have been proposed for stratifying patients into different subgroups based on such heterogeneity. However, the majority of existing methods developed for this purpose focus on the case with a dichotomous treatment and are not directly applicable to multi-arm trials. In this paper, we consider the problem of patient stratification in multi-arm trial settings and propose a two-stage procedure within the Bayesian nonparametric framework. Specifically, we first use Bayesian additive regression trees (BART) to predict potential outcomes (treatment responses) under different treatment options for each patient, and then we leverage Bayesian profile regression to cluster patients into subgroups according to their baseline characteristics and predicted potential outcomes. We further embed a variable selection procedure into our proposed framework to identify the patient characteristics that actively "drive" the clustering structure. We conduct simulation studies to examine the performance of our proposed method and demonstrate the method by applying it to a UK-based multi-arm blood donation trial, wherein our method uncovers five clinically meaningful donor subgroups.

翻译：精准医学是一个新兴领域，通过考虑个体异质性来优化临床实践。在临床试验中，治疗效应异质性的评估是重要组成部分，近年来已涌现出许多基于此类异质性对患者进行分组的统计方法。然而，现有方法大多针对二分类治疗情形设计，难以直接适用于多臂试验。本文探讨多臂试验场景下的患者分层问题，并基于贝叶斯非参数框架提出一种两阶段方法。具体而言，我们首先采用贝叶斯加性回归树（BART）预测每位患者在不同治疗方案下的潜在结果（治疗反应），进而利用贝叶斯轮廓回归根据患者的基线特征和预测的潜在结果将其聚类为亚组。此外，我们在该框架中嵌入变量选择过程，以识别主动"驱动"聚类结构的患者特征。通过模拟研究评估方法性能，并将其应用于英国一项多臂献血试验，该方法识别出五个具有临床意义的献血者亚组。