Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States.
Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, United States.
Biometrics. 2024 Oct 3;80(4). doi: 10.1093/biomtc/ujae123.
A stepped wedge design is an unidirectional crossover design where clusters are randomized to distinct treatment sequences. While model-based analysis of stepped wedge designs is a standard practice to evaluate treatment effects accounting for clustering and adjusting for covariates, their properties under misspecification have not been systematically explored. In this article, we focus on model-based methods, including linear mixed models and generalized estimating equations with an independence, simple exchangeable, or nested exchangeable working correlation structure. We study when a potentially misspecified working model can offer consistent estimation of the marginal treatment effect estimands, which are defined nonparametrically with potential outcomes and may be functions of calendar time and/or exposure time. We prove a central result that consistency for nonparametric estimands usually requires a correctly specified treatment effect structure, but generally not the remaining aspects of the working model (functional form of covariates, random effects, and error distribution), and valid inference is obtained via the sandwich variance estimator. Furthermore, an additional g-computation step is required to achieve model-robust inference under non-identity link functions or for ratio estimands. The theoretical results are illustrated via several simulation experiments and re-analysis of a completed stepped wedge cluster randomized trial.
阶梯式楔形设计是一种单向交叉设计，其中集群被随机分配到不同的治疗序列。虽然基于模型的阶梯式楔形设计分析是评估治疗效果的标准方法，可考虑聚类并调整协变量，但它们在指定不当的情况下的特性尚未得到系统探索。在本文中，我们专注于基于模型的方法，包括线性混合模型和广义估计方程，具有独立性、简单交换或嵌套交换的工作相关结构。我们研究了潜在的指定不当的工作模型如何能够提供边缘治疗效果估计值的一致估计，这些估计值是通过潜在结果非参数定义的，并且可能是日历时间和/或暴露时间的函数。我们证明了一个中心结果，即对于非参数估计值，一致性通常需要正确指定治疗效果结构，但通常不需要工作模型的其余方面（协变量的函数形式、随机效应和误差分布），并且通过三明治方差估计器可以获得有效推断。此外，在非恒等链接函数或比率估计值的情况下，需要进行额外的 g 计算步骤以实现模型稳健推断。通过几个模拟实验和已完成的阶梯式楔形集群随机试验的重新分析来说明理论结果。
