G中的饱和性：带信息性集群规模的集群随机试验中简单且稳健的因果推断
Saturation in G: simple & robust causal inference in cluster randomized trials with informative cluster sizes
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中文总结 AI 辅助
针对带信息性集群规模的集群随机试验，提出带g计算的集群规模饱和模型，可一致估计个体与集群平均处理效应，在模拟及真实数据中表现出无偏、高效等优势。
中文摘要 AI 辅助
集群随机试验（CRTs）可能存在信息性集群规模（ICS），即集群规模与结局和/或处理效应相关。在ICS下，个体和集群平均处理效应（iATE、cATE）可能出现差异，而传统线性混合效应模型（LMM）与采用可交换工作相关结构的广义估计方程（GEE）会产生依赖于数据的加权对比，该对比对这两种估计量均不一致。针对这类存在ICS的场景，我们提出易于实施的“带g计算的集群规模饱和模型”（CS-g），该模型对标准做法采用两步简单调整：（1）在适当加权的工作LMM或GEE中加入饱和连续型集群规模主效应及处理×集群规模交互项；（2）应用g计算以目标可解释的边际估计量。我们证明，适当加权的带g计算的集群规模饱和LMM，以及更通用的带g计算的集群规模饱和GEE，可在广泛的可解释估计量中一致地目标iATE和cATE，同时允许存在ICS。关键的是，这种一致性在其他模型组件的任意误设（包括饱和集群规模项的函数形式）下均成立。此外，我们证明这些一致的CS-g估计量与其模型稳健标准化对应量存在精确的有限样本等价性。在连续型和二值型结局的模拟中，所提出的CS-g估计量无偏、比其他一致估计量更有效，且检测ICS的功效更高。对PPACT P-CRT的再分析进一步说明了该方法。总体而言，CS-g为在存在ICS的P-CRT中目标可解释的边际效应提供了一种简单、稳健且高效的途径。
英文摘要
Cluster randomized trials (CRTs) can exhibit informative cluster sizes (ICS) where cluster size is associated with outcomes and/or treatment effects. Under ICS, the individual and cluster-average treatment effects (iATE, cATE) can diverge, and the conventional linear mixed-effects model (LMM) and generalized estimating equation (GEE) with an exchangeable working correlation can produce data-dependent weighted contrasts that are not consistent for either estimand. In these settings with ICS, we propose easy to implement "cluster-size saturated models with g-computation" (CS-g), which employ a simple two-step adjustment to standard practice: (1.) augment the appropriately weighted working LMM or GEE with a saturated continuous cluster-size main effect and treatment x cluster-size interaction, and (2.) apply g-computation to target an interpretable marginal estimand. We prove that the appropriately weighted cluster-size saturated LMM with g-computation and more general cluster-size saturated GEE with g-computation can consistently target the iATE and cATE, among a broad class of interpretable estimands, while allowing for ICS. Crucially, this consistency holds under arbitrary misspecification of other model components, including the functional form of the saturated cluster-size terms. Furthermore, we demonstrate exact finite-sample equivalence between these consistent CS-g estimators and their model-robust standardization counterparts. Across simulations with continuous and binary outcomes, the proposed CS-g estimators were unbiased, more efficient than other consistent estimators, and returned greater power to detect ICS. A re-analysis of the PPACT P-CRT further illustrates the approach. Altogether, CS-g offers a simple, robust, and efficient route to target interpretable marginal effects in P-CRTs with ICS.
发表机构
- University of Pennsylvania（宾夕法尼亚大学）
- Yale School of Public Health（耶鲁公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。
