ArticleInternational journal of epidemiology2023
Estimating intra-cluster correlation coefficients for planning longitudinal cluster randomized trials: a tutorial.
Article in International journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Impact of Rural Trauma Team Development Education on Prehospital Time, Referral-to-Dispatch Interval, and Neurological and Musculoskeletal Injury Outcomes: Cluster Randomized Controlled Trial.JMIR human factors · 2026Trial
- mHealth to support resistance training using outdoor gyms: the ecofit hybrid type 3 implementation-effectiveness trial.Translational behavioral medicine · 2026Trial
- Evaluation of injury prevention interventions using the stepped wedge cluster randomised trial design: key considerations.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2026Article
- Prevalence of hypertension, diabetes, and associated risk factors in a rural community in Western Kenya: a population-based cross-sectional survey.BMC public health · 2026Article
- Optimal Designs in Open-Cohort Longitudinal Cluster Randomized Trials With a Continuous Outcome.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Enhancing uptake of the Canadian Prison Needle Exchange Program using a non-randomized stepped-wedge cluster type 1 hybrid implementation trial: The NEXUS study protocol.Contemporary clinical trials communications · 2026Article
- Model-robust standardization in stepped wedge cluster randomized trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 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
- Network generalized estimating equations for complexly correlated data with applications to cluster randomized trials.Biostatistics (Oxford, England) · 2025Article
- Article
- Selecting the optimal longitudinal cluster randomized design with a continuous outcome: Parallel-arm, crossover, or stepped-wedge.Statistical methods in medical research · 2025Article
- Power analysis for concurrent balanced or imbalanced multiple-intervention stepped wedge design: a simulation-based approach.BMC medical research methodology · 2025Article
- Power calculation for cross-sectional stepped wedge cluster randomized trials with a time-to-event endpoint.Biometrics · 2025Article
- Article
- 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
- Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs.Statistical methods in medical research · 2024Article
- Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes.Statistics in medicine · 2023Article
- Sample size determination for external pilot cluster randomised trials with binary feasibility outcomes: a tutorial.Pilot and feasibility studies · 2023Article
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4 authors.
Funding
Abstract
It is well-known that designing a cluster randomized trial (CRT) requires an advance estimate of the intra-cluster correlation coefficient (ICC). In the case of longitudinal CRTs, where outcomes are assessed repeatedly in each cluster over time, estimates for more complex correlation structures are required. Three common types of correlation structures for longitudinal CRTs are exchangeable, nested/block exchangeable and exponential decay correlations-the latter two allow the strength of the correlation to weaken over time. Determining sample sizes under these latter two structures requires advance specification of the within-period ICC and cluster autocorrelation coefficient as well as the intra-individual autocorrelation coefficient in the case of a cohort design. How to estimate these coefficients is a common challenge for investigators. When appropriate estimates from previously published longitudinal CRTs are not available, one possibility is to re-analyse data from an available trial dataset or to access observational data to estimate these parameters in advance of a trial. In this tutorial, we demonstrate how to estimate correlation parameters under these correlation structures for continuous and binary outcomes. We first introduce the correlation structures and their underlying model assumptions under a mixed-effects regression framework. With practical advice for implementation, we then demonstrate how the correlation parameters can be estimated using examples and we provide programming code in R, SAS, and Stata. An Rshiny app is available that allows investigators to upload an existing dataset and obtain the estimated correlation parameters. We conclude by identifying some gaps in the literature.
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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.