Model-robust standardization in stepped wedge cluster randomized trials
针对阶梯楔形整群随机试验，提出一种模型稳健的标准化框架，定义四种因果估计量，并给出即使工作模型设定错误也能得到一致估计的简单方法，附有R包实现。
Abstract Stepped-wedge cluster-randomized trials (SW-CRTs) are widely used in healthcare and implementation science, enabling all clusters to receive the intervention through a staggered rollout. Traditional model-based methods, including generalized estimating equations and mixed models, yield estimates that depend on implicit weighting schemes and parametric assumptions, and therefore may target ambiguous estimands under model misspecification. In this article, we propose a model-robust standardization framework for SW-CRTs that generalizes existing methods from parallel-arm CRTs to address informative sizes. We define causal estimands including horizontal-individual, horizontal-cluster, vertical-individual, and vertical-cluster average treatment effects under a super population framework and introduce a simple procedure that standardizes parametric and semiparametric working models for estimand-aligned analysis. For any specified working model, the resulting estimators remain consistent for their target estimands even if the working regression model is misspecified; moreover, their efficiency improves as the working model more closely approximates the true data-generating process. We evaluate the finite-sample properties of our proposed estimators through extensive simulations. The proposed methods are implemented in the MRStdLCRT R package on CRAN. Finally, we illustrate the application of our methods through reanalyses of two real-world SW-CRTs.