10/07/2025
This Thursday, October 9th, KC Gary Chan, PhD of the University of Washington School of Public Health will present a Levin Lecture on “Robust and efficient semiparametric inference for the stepped wedge design” from 11:45am - 1:00pm over Zoom. You can find the link on the Fall 2025 Departmental Lectures page. All are welcome to come learn with us!
Abstract:
Stepped wedge designs (SWDs) are increasingly used to evaluate longitudinal cluster-level interventions but pose substantial challenges for valid inference. Because crossover times are randomized, intervention effects are intrinsically confounded with secular time trends, while heterogeneous cluster effects, complex correlation structures, baseline covariate imbalances, and unreliable standard errors from few clusters further complicate statistical inference. We propose a unified semiparametric framework for estimating possibly time-varying intervention effects in SWDs that directly addresses these issues. A nonstandard development of semiparametric efficiency theory is required to accommodate correlated observations within clusters, non-identically distributed outcomes across clusters due to varying cluster-period sizes, and weakly dependent treatment assignments that are hallmarks of SWDs. The resulting estimator of treatment contrast is consistent and asymptotically normal even under misspecification of the covariance structure and control cluster-period means, and achieves the semiparametric efficiency bound when both are correctly specified. To facilitate inference for trials with few clusters, we introduce a permutation-based procedure to better capture finite-sample variability and a leave-one-out correction to mitigate plug-in bias. We further discuss how effect modification can be naturally incorporated, and imbalanced precision variables can be accommodated via a simple adjustment closely related to post-stratification, a novel connection of independent interest. Simulations and application to a public health trial demonstrate the robustness and efficiency of the proposed method relative to standard approaches.
