Defining and Estimating Effects in Cluster Randomized Trials: A Methods Comparison
整群随机试验中的效应定义与估计：方法比较AI 解读 ↓
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整群随机试验中的效应定义与估计：方法比较
- 研究问题
- 整群随机试验中，个体层面与群组层面的因果目标有何差别，群组数量有限且规模不同时，常用估计方法表现如何。
- 方法与思路
- 作者以反事实结局汇总量定义多类因果效应，比较t检验、GEE、增广GEE和TMLE，并进行有限样本模拟及早产干预应用。
- 材料陈述的贡献
- 摘要称，应用中群组与个体层面相对效应分别为0.81和0.66，显示目标选择影响结果；作者认为TMLE兼具灵活性和精度潜力。
- 阅读关注点
- 应核对两类目标的权重和分母、群组规模与结局的关系，以及有限群组推断、协变量调整和第一类错误控制所依赖的条件。
- 阅读边界
- 这是arXiv预印本，同行评审或期刊接收未核实；摘要未给出区间和具体理论条件，应用中的目标差异不能视为估计方法优劣证明。
原文摘要
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.