Cluster-randomized trials (CRTs) are widely used to evaluate interventions delivered at the clinic, practice, or community level. Although standard analyses typically target average treatment effects, such summaries mask potentially meaningful variation in treatment response across individuals and clusters. This work addresses the estimation of conditional average treatment effects (CATEs) for continuous outcomes in two-arm parallel CRTs by defining causal estimands that incorporate both individual- and cluster-level baseline covariates while marginalizing over unobserved cluster heterogeneity. To estimate these quantities, we develop a unified framework based on mixed-effects machine learning, integrating and extending a range of existing approaches, including Bayesian additive regression trees with random effects, multilevel Bayesian causal forests, mixed-effects random forests, several mixed-effects gradient boosting procedures, and generalized additive mixed models, while incorporating cluster-specific random intercepts to account for within-cluster dependence. We evaluate these methods across diverse simulation scenarios and demonstrate their use in the Task Shifting and Blood Pressure Control in Ghana CRT, which investigates strategies for improving hypertension management. Drawing on these investigations, we provide practical guidance for applying mixed-effects machine learning to quantify treatment-effect heterogeneity in CRTs, together with reproducible code that enables investigators to implement all methods within a coherent workflow.

翻译：整群随机试验（CRT）被广泛用于评估在诊所、实践或社区层面实施的干预措施。尽管标准分析通常针对平均处理效应，但此类汇总可能掩盖个体和群组间处理反应中潜在的重要变异性。本研究致力于估计双臂平行CRT中连续结局的条件平均处理效应（CATE），通过定义纳入个体和群组层面基线协变量、同时边缘化未观测的群组异质性的因果估计量。为估计这些量，我们开发了一个基于混合效应机器学习的统一框架，整合并扩展了一系列现有方法，包括含随机效应的贝叶斯加性回归树、多水平贝叶斯因果森林、混合效应随机森林、多种混合效应梯度提升程序及广义可加混合模型，同时纳入群组特异性随机截距以解释群组内相关性。我们在多种模拟场景下评估这些方法，并在加纳任务转移与血压控制CRT中展示其应用，该试验探讨改善高血压管理的策略。基于这些研究，我们为应用混合效应机器学习量化CRT中处理效应异质性提供了实用指导，并附有可复现代码，使研究者能够在连贯工作流程中实施所有方法。