Across research disciplines, cluster randomized trials (CRTs) are commonly implemented to evaluate interventions delivered to groups of participants, such as communities and clinics. Despite advances in the design and analysis of CRTs, several challenges remain. First, there are many possible ways to specify the causal effect of interest (e.g., at the individual-level or at the cluster-level). Second, the theoretical and practical performance of common methods for CRT analysis remain poorly understood. Here, we present a general framework to formally define an array of causal effects in terms of summary measures of counterfactual outcomes. Next, we provide a comprehensive overview of CRT estimators, including the t-test, generalized estimating equations (GEE), augmented-GEE, and targeted maximum likelihood estimation (TMLE). Using finite sample simulations, we illustrate the practical performance of these estimators for different causal effects and when, as commonly occurs, there are limited numbers of clusters of different sizes. Finally, our application to data from the Preterm Birth Initiative (PTBi) study demonstrates the real-world impact of varying cluster sizes and targeting effects at the cluster-level or at the individual-level. Specifically, the relative effect of the PTBI intervention was 0.81 at the cluster-level, corresponding to a 19% reduction in outcome incidence, and was 0.66 at the individual-level, corresponding to a 34% reduction in outcome risk. Given its flexibility to estimate a variety of user-specified effects and ability to adaptively adjust for covariates for precision gains while maintaining Type-I error control, we conclude TMLE is a promising tool for CRT analysis.

翻译：跨学科研究中，整群随机试验（CRT）常被用于评估针对社区、诊所等群体参与者实施的干预措施。尽管CRT的设计与分析方法已有进展，但仍存在若干挑战。首先，感兴趣的因果效应存在多种定义方式（如个体层面或整群层面）。其次，常见CRT分析方法的理论与实际表现仍未被充分理解。本文首先提出一个通用框架，通过反事实结局的汇总指标正式定义一系列因果效应。随后，我们全面概述CRT估计量，包括t检验、广义估计方程（GEE）、增广GEE及目标最大似然估计（TMLE）。通过有限样本模拟，我们展示了这些估计量在不同因果效应下、以及常见的小规模且大小不等的整群场景中的实际表现。最后，我们将方法应用于早产儿倡议（PTBi）研究数据，揭示了整群规模差异及靶向整群层面或个体层面效应的实际影响。具体而言，PTBi干预在整群层面的相对效应为0.81（对应结局发生率降低19%），在个体层面为0.66（对应结局风险降低34%）。鉴于TMLE在估计用户指定多种效应时的灵活性、通过自适应协变量调整提升精度并控制I类错误的能力，我们得出结论：TMLE是CRT分析中极具前景的工具。