Stepped wedge cluster randomized experiments represent a class of unidirectional crossover designs 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 introduce four possible 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 the sense that its point estimator is consistent with 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了一类单向交叉设计，越来越多地被用于比较效果和实施科学研究。尽管阶梯楔形整群随机实验已变得流行，但对估计量的定义以及针对明确定义估计量的稳健方法仍不充分。为弥补这一不足，我们描述了一类显式承认阶梯楔形整群随机实验中多层级数据结构的估计量，并强调了该类估计量中三个具有可解释性和实际意义的典型成员。随后，我们引入了四种可能的协方差分析（ANCOVA）工作模型表述，以实现与估计量一致的分析。通过利用基线协变量，每种ANCOVA模型相较于未调整的估计量均有潜力提高估计效率。此外，每个ANCOVA估计量是"模型辅助"的，即即使工作模型被误设，其点估计量仍与目标估计量一致。在阶梯楔形随机化方案下，我们为每个估计量建立了有限总体中心极限定理，这为基于设计的方差估计量提供了理论基础。通过模拟，我们研究了不同数据生成过程下ANCOVA估计量的有限样本操作特性。我们还通过分析华盛顿州快速伴侣治疗研究，展示了这些估计量的实际应用。