  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.
