ArticleStatistics in medicine2020
Sample size requirements for detecting treatment effect heterogeneity in 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 26 papers.
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Who cites it
26 citing papers in PubMed, 36 citations in OpenAlex.
- Rethinking children's physical activity interventions at school: A new context-specific approach.Frontiers in public health · 2023Trial
- 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
- Article
- 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
- Article
- Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials.Biometrika · 2026Article
- Group Sequential Design and Monitoring of Clustered Data in Randomized Eye Trials.Statistics in biopharmaceutical research · 2025Article
- Permutation tests for detecting treatment effect heterogeneity in cluster randomized trials.Statistical methods in medical research · 2025Article
- Article
- Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials.Clinical trials (London, England) · 2024Article
- Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs.Statistical methods in medical research · 2024Article
- Using Power Analysis to Choose the Unit of Randomization, Outcome, and Approach for Subgroup Analysis for a Multilevel Randomized Controlled Clinical Trial to Reduce Disparities in Cardiovascular Health.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials.Statistical methods in medical research · 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
- 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
- Designing three-level cluster randomized trials to assess treatment effect heterogeneity.Biostatistics (Oxford, England) · 2023Article
- Estimating intra-cluster correlation coefficients for planning longitudinal cluster randomized trials: a tutorial.International journal of epidemiology · 2023Article
- Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity.Statistics in medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Cluster randomized trials (CRTs) refer to experiments with randomization carried out at the cluster or the group level. While numerous statistical methods have been developed for the design and analysis of CRTs, most of the existing methods focused on testing the overall treatment effect across the population characteristics, with few discussions on the differential treatment effect among subpopulations. In addition, the sample size and power requirements for detecting differential treatment effect in CRTs remain unclear, but are helpful for studies planned with such an objective. In this article, we develop a new sample size formula for detecting treatment effect heterogeneity in two-level CRTs for continuous outcomes, continuous or binary covariates measured at cluster or individual level. We also investigate the roles of two intraclass correlation coefficients (ICCs): the adjusted ICC for the outcome of interest and the marginal ICC for the covariate of interest. We further derive a closed-form design effect formula to facilitate the application of the proposed method, and provide extensions to accommodate multiple covariates. Extensive simulations are carried out to validate the proposed formula in finite samples. We find that the empirical power agrees well with the prediction across a range of parameter constellations, when data are analyzed by a linear mixed effects model with a treatment-by-covariate interaction. Finally, we use data from the HF-ACTION study to illustrate the proposed sample size procedure for detecting heterogeneous treatment effects.
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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.