标题： 模型稳健的阶梯楔形设计标准化 显示英文标题

标题： Model-robust standardization in stepped wedge designs

摘要： 分阶段楔形集群随机试验（SW-CRTs）在医疗保健和实施科学中被广泛使用，通过确保所有集群最终接受干预，提供了伦理上的优势。 处理的分阶段实施引入了定义和估计处理效果估计量的复杂性，特别是在信息性规模下。 传统基于模型的方法，包括广义估计方程（GEE）和线性混合模型（LMM），其估计结果依赖于隐含的加权方案和参数假设，在存在信息性规模时会导致不同类型的估计量出现偏差。 尽管最近的方法尝试在SW-CRTs中提供稳健估计，但它们在建模假设上存在限制，或缺乏在信息性规模下一致估计多个估计量的通用框架。 在本文中，我们提出了一种针对SW-CRTs的模型稳健标准化框架，该框架从平行臂CRTs的现有方法进行了推广。 我们在超级总体框架下定义了因果估计量，包括水平个体、水平集群、垂直个体和垂直集群的平均处理效应，并引入了一种增强的标准化估计量，该估计量对参数和半参数工作模型进行标准化，同时在任意错误设定下保持对信息性集群规模的稳健性。 我们通过广泛的模拟研究评估了所提出的估计量的小样本特性，评估了它们在各种SW-CRT设计下的表现。 最后，我们通过重新分析两个现实世界的SW-CRT来说明模型稳健估计的实际应用。 显示英文摘要

摘要： Stepped-wedge cluster-randomized trials (SW-CRTs) are widely used in healthcare and implementation science, providing an ethical advantage by ensuring all clusters eventually receive the intervention. The staggered rollout of treatment introduces complexities in defining and estimating treatment effect estimands, particularly under informative sizes. Traditional model-based methods, including generalized estimating equations (GEE) and linear mixed models (LMM), produce estimates that depend on implicit weighting schemes and parametric assumptions, leading to bias for different types of estimands in the presence of informative sizes. While recent methods have attempted to provide robust estimation in SW-CRTs, they are restrictive on modeling assumptions or lack of general framework for consistent estimating multiple estimands under informative size. In this article, we propose a model-robust standardization framework for SW-CRTs that generalizes existing methods from parallel-arm CRTs. We define causal estimands including horizontal-individual, horizontal-cluster, vertical-individual, and vertical-cluster average treatment effects under a super population framework and introduce an augmented standardization estimator that standardizes parametric and semiparametric working models while maintaining robustness to informative cluster size under arbitrary misspecification. We evaluate the finite-sample properties of our proposed estimators through extensive simulation studies, assessing their performance under various SW-CRT designs. Finally, we illustrate the practical application of model-robust estimation through a reanalysis of two real-world SW-CRTs.