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 intervention effect (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 use causal models to formally define an array of causal effects as summary measures of counterfactual outcomes. Next, we provide a comprehensive overview of well-known CRT estimators, including the t-test and generalized estimating equations (GEE), as well as less known methods, including augmented-GEE and targeted maximum likelihood estimation (TMLE). In finite sample simulations, we illustrate the performance of these estimators and the importance of effect specification, especially when cluster size varies. Finally, our application to data from the Preterm Birth Initiative (PTBi) study demonstrates the real-world importance of selecting an analytic approach corresponding to the research question. Given its flexibility to estimate a variety of 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:在研究领域各学科之间,通常采用集群随机试验(CRTs)来评价向社区和诊所等参与者群体提供的干预措施。尽管在设计和分析CRTs方面有所进展,但仍存在若干挑战。首先,有许多可能的方法可以具体说明干预效果(例如,在个人一级或集群一级)。第二,CRT分析的共同方法的理论和实际表现仍然不甚了解。在这里,我们使用因果模型正式确定一系列因果关系,作为反事实结果的总结措施。其次是,我们全面概述众所周知的CRT估计数据,包括测试和普遍估计方程(GEEE),以及不太为人所知的方法,包括扩大的GEEE和有针对性的最大可能性估计(TMLE)。在有限的抽样模拟中,我们展示了这些估计者的表现以及效果规格的重要性,特别是在集群大小不同的情况下。最后,我们对从PTBiter出生倡议(PTBi)研究中的数据的应用表明,选择与研究问题相对应的分析性方法(GEEEEE)的重要性,包括扩大的GEEEE和有针对性的最大可能性估计(TM),同时,我们估算CLI在适应性影响和共同分析方面有希望的精确性的能力。