Model-assisted analysis of covariance estimators for stepped wedge cluster randomized experiments

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.

References in corpus (3)

• Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's critique
• The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation
• Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect

Cited by in corpus (1)

• How should parallel cluster randomized trials with a baseline period be analyzed? A survey of estimands and common estimators