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

TL;DR

This paper develops a framework for analyzing covariance estimators in stepped wedge cluster randomized experiments, addressing definitional gaps and proposing robust, model-assisted methods with proven theoretical properties.

Contribution

It introduces a class of estimands suitable for SW-CREs and proposes four ANCOVA-based estimators that are consistent even under model misspecification.

Findings

Establishes finite population CLT for the estimators

Demonstrates estimator robustness through simulations

Applies methods to real Washington State study data

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.