Stepped wedge cluster randomized experiments represent a class of unidirectional crossover designs that are increasingly adopted for comparative effectiveness and implementation science research. Although stepped wedge cluster randomized experiments have become popular, definitions of estimands and robust methods to target clearly-defined estimands remain insufficient. To address this gap, we describe a class of estimands that explicitly acknowledge the multilevel data structure in stepped wedge cluster randomized experiments, and highlight three typical members of the estimand class that are interpretable and are of practical interest. We then discuss four formulations of analysis of covariance (ANCOVA) working models to achieve estimand-aligned analyses. By exploiting baseline covariates, each ANCOVA model can potentially improve the estimation efficiency over the unadjusted estimators. In addition, each ANCOVA estimator is model-assisted in a sense that its point estimator is consistent to the target estimand even when the working model is misspecified. Under the stepped wedge randomization scheme, we establish the finite population Central Limit Theorem for each estimator, which motivates design-based variance estimators. Through simulations, we study the finite-sample operating characteristics of the ANCOVA estimators under different data generating processes. We illustrate their applications via the analysis of the Washington State Expedited Partner Therapy study.

翻译：阶梯楔形整群随机实验代表一类单向交叉设计，正日益被比较效果研究和实施科学研究领域所采纳。尽管阶梯楔形整群随机实验已广泛流行，但有关目标估计量的定义以及针对明确界定的估计量的稳健方法仍显不足。为此，本文首先描述一类明确考虑阶梯楔形整群随机实验中多层次数据结构的估计量，并重点介绍该类估计量中三个具有可解释性和实践意义的典型成员。随后讨论四种协方差分析(ANCOVA)工作模型的公式化表达，以实现与估计量对齐的分析。通过利用基线协变量，每种ANCOVA模型相较于未调整估计量均可能提升估计效率。此外，每种ANCOVA估计量具有模型辅助性质：即使工作模型设定错误，其点估计量仍对目标估计量保持一致性。基于阶梯楔形随机化方案，我们为每个估计量建立了有限总体中心极限定理，进而推导出基于设计的方差估计量。通过仿真研究，我们考察了不同数据生成过程中ANCOVA估计量的有限样本操作特性，并借助华盛顿州快速伴侣治疗研究的数据分析阐释其应用。