Model‐assisted analysis of covariance estimators for stepped wedge cluster randomized experiments
本文针对阶梯楔形整群随机实验，定义了考虑多层数据结构的可解释目标量，并提出了四种协方差分析工作模型，其估计量即使模型设定错误也保持一致，通过模拟和实际数据分析验证了性能。
Abstract Stepped wedge cluster randomized experiments (SW‐CREs) represent a class of unidirectional crossover designs. Although SW‐CREs have become popular, definitions of estimands and robust methods to target estimands under the potential outcomes framework remain insufficient. To address this gap, we describe a class of estimands that explicitly acknowledge the multilevel data structure in SW‐CREs and highlight three typical members of the estimand class that are interpretable. We then introduce four analysis of covariance (ANCOVA) working models to achieve estimand‐aligned analyses with covariate adjustment. Each ANCOVA estimator is model‐assisted, as its point estimator is consistent even when the working model is misspecified. Under the stepped wedge randomization scheme, we establish the finite population Central Limit Theorem for each estimator. We study the finite‐sample operating characteristics of the ANCOVA estimators in simulations and illustrate their application by analyzing the Washington State Expedited Partner Therapy study.