Model-assisted analysis of covariance estimators for stepped wedge cluster randomized experiments
arXiv (Cornell University)(2023)
摘要
Stepped wedge cluster randomized experiments represent a class of
unidirectional crossover designs increasingly adopted for comparative
effectiveness and implementation science research. Although stepped wedge
cluster randomized experiments have become popular, definitions of estimands
and robust methods to target clearly-defined estimands remain insufficient. To
address this gap, we describe a class of estimands that explicitly acknowledge
the multilevel data structure in stepped wedge cluster randomized experiments,
and highlight three typical members of the estimand class that are
interpretable and are of practical interest. We then introduce four possible
formulations of analysis of covariance (ANCOVA) working models to achieve
estimand-aligned analyses. By exploiting baseline covariates, each ANCOVA model
can potentially improve the estimation efficiency over the unadjusted
estimators. In addition, each ANCOVA estimator is model-assisted in the sense
that its point estimator is consistent with the target estimand even when the
working model is misspecified. Under the stepped wedge randomization scheme, we
establish the finite population Central Limit Theorem for each estimator, which
motivates design-based variance estimators. Through simulations, we study the
finite-sample operating characteristics of the ANCOVA estimators under
different data-generating processes. We illustrate their applications via the
analysis of the Washington State Expedited Partner Therapy study.
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关键词
covariance estimators,stepped wedge
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