ArticleStatistics in medicine2020
Sample size and power calculations for open cohort longitudinal cluster randomized trials.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Effectiveness of Exercise in Older Adults Discharged From the ER After Minor Injuries: The CEDeComS Stepped-Wedge Trial.Journal of the American Geriatrics Society · 2026Trial
- Does it decay? Obtaining decaying correlation parameter values from previously analysed cluster randomised trials.Statistical methods in medical research · 2023Trial
- The batched stepped wedge design: A design robust to delays in cluster recruitment.Statistics in medicine · 2022Trial
- Repeated sampling of different individuals within the same clusters to improve precision of longitudinal estimators: the DISC design.International journal of epidemiology · 2026Article
- Optimal Designs in Open-Cohort Longitudinal Cluster Randomized Trials With a Continuous Outcome.Biometrical journal. Biometrische Zeitschrift · 2026Article
- A tutorial on conducting sample size and power calculations for detecting treatment effect heterogeneity in cluster randomized trials with linear mixed models.International journal of epidemiology · 2026Article
- Analysis of Stepped-Wedge Cluster Randomized Trials: A Tutorial Using Marginal Models.Statistics in medicine · 2026Article
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- Article
- Selecting the optimal longitudinal cluster randomized design with a continuous outcome: Parallel-arm, crossover, or stepped-wedge.Statistical methods in medical research · 2025Article
- Article
- Identifying Less Burdensome and More Cost-Efficient Incomplete Stepped Wedge Designs for Continuous Outcomes Collected via Repeated Cross-Sections.Statistics in medicine · 2025Article
- Designing stepped wedge trials to evaluate physical activity interventions in schools: methodological considerations.The international journal of behavioral nutrition and physical activity · 2025Article
- Optimal design of cluster randomized crossover trials with a continuous outcome: Optimal number of time periods and treatment switches under a fixed number of clusters or fixed budget.Behavior research methods · 2024Article
- 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
- Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials.Statistical methods in medical research · 2024Article
- Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity.Statistics in medicine · 2024Article
- Article
- Estimating intra-cluster correlation coefficients for planning longitudinal cluster randomized trials: a tutorial.International journal of epidemiology · 2023Article
- Key considerations for designing, conducting and analysing a cluster randomized trial.International journal of epidemiology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
When calculating sample size or power for stepped wedge or other types of longitudinal cluster randomized trials, it is critical that the planned sampling structure be accurately specified. One common assumption is that participants will provide measurements in each trial period, that is, a closed cohort, and another is that each participant provides only one measurement during the course of the trial. However some studies have an "open cohort" sampling structure, where participants may provide measurements in variable numbers of periods. To date, sample size calculations for longitudinal cluster randomized trials have not accommodated open cohorts. Feldman and McKinlay (1994) provided some guidance, stating that the participant-level autocorrelation could be varied to account for the degree of overlap in different periods of the study, but did not indicate precisely how to do so. We present sample size and power formulas that allow for open cohorts and discuss the impact of the degree of "openness" on sample size and power. We consider designs where the number of participants in each cluster will be maintained throughout the trial, but individual participants may provide differing numbers of measurements. Our results are a unification of closed cohort and repeated cross-sectional sample results of Hooper et al (2016), and indicate precisely how participant autocorrelation of Feldman and McKinlay should be varied to account for an open cohort sampling structure. We discuss different types of open cohort sampling schemes and how open cohort sampling structure impacts on power in the presence of decaying within-cluster correlations and autoregressive participant-level errors.
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