A stepped wedge design is a unidirectional crossover design where clusters are randomized to distinct treatment sequences defined by calendar time. While model-based analysis of stepped wedge designs -- via linear mixed models or generalized estimating equations -- is standard practice to evaluate treatment effects accounting for clustering and adjusting for baseline covariates, formal results on their model-robustness properties remain unavailable. In this article, we study when a potentially misspecified multilevel model can offer consistent estimators for treatment effect estimands that are functions of calendar time and/or exposure time. We describe a super-population potential outcomes framework to define treatment effect estimands of interest in stepped wedge designs, and adapt linear mixed models and generalized estimating equations to achieve estimand-aligned inference. We prove a central result that, as long as the treatment effect structure is correctly specified in each working model, our treatment effect estimator is robust to arbitrary misspecification of all remaining model components. The theoretical results are illustrated via simulation experiments and re-analysis of a cardiovascular stepped wedge cluster randomized trial.

翻译：阶梯楔形设计是一种单向交叉设计，其中集群被随机分配到由日历时间定义的特定治疗序列。虽然基于模型分析阶梯楔形设计——通过线性混合模型或广义估计方程——是评估治疗效果、考虑聚类效应并调整基线协变量的标准实践，但其模型稳健性特性的正式结果尚不可得。本文研究了潜在设定错误的多层模型何时能够为作为日历时间和/或暴露时间函数的治疗效果目标量提供一致估计量。我们描述了一个超总体潜在结果框架，用于定义阶梯楔形设计中感兴趣的治疗效果目标量，并调整线性混合模型和广义估计方程以实现目标对齐推断。我们证明了一个核心结果：只要在每个工作模型中正确指定了治疗效果结构，我们的治疗效果估计量对剩余所有模型组件的任意设定错误均具有稳健性。理论结果通过模拟实验和一项心血管阶梯楔形集群随机试验的再分析进行了说明。