ArticleBiometrics2023
Sample size considerations for stepped wedge designs with subclusters.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 19 citations in OpenAlex.
- The design of embedded pragmatic clinical trials: methodological developments and statistical lessons learned from the first cycle of the NIA IMPACT collaboratory.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Designing Cluster Randomized Trials: A Practical Guide for Investigators.Medical care · 2026Article
- Analysis of Stepped-Wedge Cluster Randomized Trials: A Tutorial Using Marginal Models.Statistics in medicine · 2026Article
- Designing Stepped Wedge Cluster Randomized Trials With a Baseline Measurement of the Outcome.Statistics in medicine · 2025Article
- Power calculation for cross-sectional stepped wedge cluster randomized trials with a time-to-event endpoint.Biometrics · 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
- Sample Size Calculations for Stepped Wedge Designs with Treatment Effects that May Change with the Duration of Time under Intervention.Prevention science : the official journal of the Society for Prevention Research · 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
- Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes.Statistics in medicine · 2023Article
- Generalizing the information content for stepped wedge designs: A marginal modeling approach.Scandinavian journal of statistics, theory and applications · 2023Article
- A user-centred implementation strategy for tuberculosis contact investigation in Uganda: protocol for a stepped-wedge, cluster-randomised trial.BMC public health · 2023Article
- A user-centred implementation strategy for tuberculosis contact investigation in Uganda: Protocol for a stepped-wedge, cluster-randomised trial.Research square · 2023Article
- Power analyses for stepped wedge designs with multivariate continuous outcomes.Statistics in medicine · 2023Article
- Sample size calculators for planning stepped-wedge cluster randomized trials: a review and comparison.International journal of epidemiology · 2022Review
- Calculating power for multilevel implementation trials in mental health: Meaningful effect sizes, intraclass correlation coefficients, and proportions of variance explained by covariates.Implementation research and practiceArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
The stepped wedge cluster randomized trial (SW-CRT) is an increasingly popular design for evaluating health service delivery or policy interventions. An essential consideration of this design is the need to account for both within-period and between-period correlations in sample size calculations. Especially when embedded in health care delivery systems, many SW-CRTs may have subclusters nested in clusters, within which outcomes are collected longitudinally. However, existing sample size methods that account for between-period correlations have not allowed for multiple levels of clustering. We present computationally efficient sample size procedures that properly differentiate within-period and between-period intracluster correlation coefficients in SW-CRTs in the presence of subclusters. We introduce an extended block exchangeable correlation matrix to characterize the complex dependencies of outcomes within clusters. For Gaussian outcomes, we derive a closed-form sample size expression that depends on the correlation structure only through two eigenvalues of the extended block exchangeable correlation structure. For non-Gaussian outcomes, we present a generic sample size algorithm based on linearization and elucidate simplifications under canonical link functions. For example, we show that the approximate sample size formula under a logistic linear mixed model depends on three eigenvalues of the extended block exchangeable correlation matrix. We provide an extension to accommodate unequal cluster sizes and validate the proposed methods via simulations. Finally, we illustrate our methods in two real SW-CRTs with subclusters.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.