Cluster-randomized trials (CRTs) are widely used to evaluate group-level interventions and increasingly collect multiple outcomes capturing complementary dimensions of benefit and risk. Investigators often seek a single global summary of treatment effect, yet existing methods largely focus on single-outcome estimands or rely on model-based procedures with unclear causal interpretation or limited robustness. We develop a unified potential outcomes framework for generalized treatment effects with multiple outcomes in CRTs, accommodating both non-prioritized and prioritized outcome settings. The proposed cluster-pair and individual-pair causal estimands are defined through flexible pairwise contrast functions and explicitly account for potentially informative cluster sizes. We establish nonparametric estimation via weighted clustered U-statistics and derive efficient influence functions to construct covariate-adjusted estimators that integrate debiased machine learning with U-statistics. The resulting estimators are consistent and asymptotically normal, attain the semiparametric efficiency bounds under mild regularity conditions, and have analytically tractable variance estimators that are proven to be consistent under cross-fitting. Simulations and an application to a CRT for chronic pain management illustrate the practical utility of the proposed methods.

翻译：群组随机试验（CRTs）被广泛用于评估群体层面的干预措施，且越来越多地收集多个结局指标以捕捉获益与风险的不同维度。研究者通常希望获得一个单一的整体治疗效果汇总指标，然而现有方法主要集中于单结局估计量，或依赖于因果解释不明确或稳健性有限的基于模型的程序。我们为CRTs中多结局的广义治疗效果建立了一个统一的潜在结果框架，可同时适用于非优先级和优先级结局设定。所提出的群组对与个体对因果估计量通过灵活的对偶对比函数定义，并明确考虑了可能具有信息性的群组规模。我们通过加权聚类U统计量建立非参数估计，并推导有效影响函数以构建协变量调整估计量，该估计量将去偏机器学习与U统计量相结合。所得估计量具有一致性和渐近正态性，在温和的正则条件下达到半参数效率界，并具有解析可处理的方差估计量，该方差估计量在交叉拟合下被证明是一致的。模拟研究及一项慢性疼痛管理CRT的应用实例说明了所提方法的实际效用。