ArticleBiometrical journal. Biometrische Zeitschrift2022
Impact of unequal cluster sizes for GEE analyses of stepped wedge cluster randomized trials with binary outcomes.
Article in Biometrical journal. Biometrische Zeitschrift, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Model-robust standardization in stepped wedge cluster randomized trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Analysis of Stepped-Wedge Cluster Randomized Trials: A Tutorial Using Marginal Models.Statistics in medicine · 2026Article
- Network generalized estimating equations for complexly correlated data with applications to cluster randomized trials.Biostatistics (Oxford, England) · 2025Article
- Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures.Statistical methods in medical research · 2024Article
- Robust analysis of stepped wedge trials using composite likelihood models.Statistics in medicine · 2024Article
- Power calculation for detecting interaction effect in cross-sectional stepped-wedge cluster randomized trials: an important tool for disparity research.BMC medical research methodology · 2024Article
- Information content of stepped wedge designs under the working independence assumption.Journal of statistical planning and inference · 2024Article
- Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity.Statistics in medicine · 2024Article
- Estimating intra-cluster correlation coefficients for planning longitudinal cluster randomized trials: a tutorial.International journal of epidemiology · 2023Article
- Generalizing the information content for stepped wedge designs: A marginal modeling approach.Scandinavian journal of statistics, theory and applications · 2023Article
- GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials.Computer methods and programs in biomedicine · 2023Article
- Article
- Sample size calculators for planning stepped-wedge cluster randomized trials: a review and comparison.International journal of epidemiology · 2022Review
- power swgee: GEE-based power calculations in stepped wedge cluster randomized trials.The Stata journal · 2022Article
- Stepped Wedge Cluster Randomized Trials: A Methodological Overview.World neurosurgery · 2022Review
- Impact of unequal cluster sizes for GEE analyses of stepped wedge cluster randomized trials with binary outcomes.Biometrical journal. Biometrische Zeitschrift · 2022Article
- swdpwr: A SAS macro and an R package for power calculations in stepped wedge cluster randomized trials.Computer methods and programs in biomedicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The stepped wedge (SW) design is a type of unidirectional crossover design where cluster units switch from control to intervention condition at different prespecified time points. While a convention in study planning is to assume the cluster-period sizes are identical, SW cluster randomized trials (SW-CRTs) involving repeated cross-sectional designs frequently have unequal cluster-period sizes, which can impact the efficiency of the treatment effect estimator. In this paper, we provide a comprehensive investigation of the efficiency impact of unequal cluster sizes for generalized estimating equation analyses of SW-CRTs, with a focus on binary outcomes as in the Washington State Expedited Partner Therapy trial. Several major distinctions between our work and existing work include the following: (i) we consider multilevel correlation structures in marginal models with binary outcomes; (ii) we study the implications of both the between-cluster and within-cluster imbalances in sizes; and (iii) we provide a comparison between the independence working correlation versus the true working correlation and detail the consequences of ignoring correlation estimation in SW-CRTs with unequal cluster sizes. We conclude that the working independence assumption can lead to substantial efficiency loss and a large sample size regardless of cluster-period size variability in SW-CRTs, and recommend accounting for correlations in the analysis. To improve study planning, we additionally provide a computationally efficient search algorithm to estimate the sample size in SW-CRTs accounting for unequal cluster-period sizes, and conclude by illustrating the proposed approach in the context of the Washington State study.
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