Jose-Miguel Yamal | Searcharxiv

arXiv2026

A Framework for Parametric Time-Varying Treatment Effects in Multiple Intervention Stepped Wedge Design Clinical Trials in the Presence of Non-Uniform Cluster-Period Correlation Structures

Stepped wedge design (SWD) trials typically assume that treatment effects are immediate following implementation, but this assumption is often unrealistic in pragmatic settings where effects evolve over exposure time. This impact is further complicated in multiple-intervention stepped wedge designs (M-SWDs), due to their complex crossover patterns. Existing work has addressed time-varying treatment effects for main effects at the analysis stage using nonparametric approaches; however, the implications for design-stage power and estimand interpretation in M-SWDs remain largely unaddressed. We develop a unified framework that incorporates parametric exposure-response functions into a modified fixed-effects design matrix, Z*, and derive corresponding generalized least squares (GLS) expressions for treatment effect estimands, variance, power, and bias under model misspecification. Analytic and simulation results demonstrate that time-varying effects can require multiple-fold increase in the number of clusters required to achieve the nominal target power. Misspecification can also induce large bias in treatment effect estimates, with particularly pronounced and non-uniform impacts on interaction effects. These findings demonstrate that treatment effect estimands in M-SWDs depend critically on assumptions about exposure-time dynamics and that failure to account for time-varying effects can lead to substantial miscalibration of power and biased inference, affecting main and interaction effects differently.

stat.ME↗

arXiv2026

A generalized multiple-intervention stepped wedge design framework for treatment effect estimation in the presence of non-uniform cluster-period correlation structures

Existing power and design methods for multiple-intervention stepped wedge designs (M-SWDs) typically assume exchangeable cluster-period correlation, despite evidence that correlation often decays over time. Misspecification of this correlation structure can substantially distort variance estimation and power, particularly for treatment interaction effects. We develop a unified covariance framework for M-SWDs that separates intracluster correlation from an explicit cluster-period correlation matrix. This formulation accommodates exchangeable, autoregressive, and more general distance-dependent correlation structures while preserving closed-form expressions for the variance of treatment effect estimators under linear mixed models. Using analytic results and simulation studies, we demonstrate that assuming uniform correlation when the true structure is time-dependent can lead to substantial power mischaracterization. Specifically, we find that designs calibrated under independence assumptions may be overly conservative and compound symmetry can be either optimistic or conservative. These findings demonstrate the importance of explicitly modeling cluster-period correlation at the design stage of M-SWDs and provide practical guidance for power calculation and design selection in realistic settings.

stat.ME↗