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Cited by 245Statistical Methods in Clinical TrialsStatistical Methods and Bayesian InferenceAdvanced Causal Inference Techniques

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Cluster randomized trials with a small number of clusters: which analyses should be used?

Leyrat, Clémence, Morgan, Katy E, Leurent, Baptiste, Kahan, Brennan C·2018·International Journal of Epidemiology
DOI 10.1093/ije/dyx169
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Abstract

Background: Cluster randomized trials (CRTs) are increasingly used to assess the effectiveness of health interventions. Three main analysis approaches are: cluster-level analyses, mixed-models and generalized estimating equations (GEEs). Mixed models and GEEs can lead to inflated type I error rates with a small number of clusters, and numerous small-sample corrections have been proposed to circumvent this problem. However, the impact of these methods on power is still unclear. Methods: We performed a simulation study to assess the performance of 12 analysis approaches for CRTs with a continuous outcome and 40 or fewer clusters. These included weighted and unweighted cluster-level analyses, mixed-effects models with different degree-of-freedom corrections, and GEEs with and without a small-sample correction. We assessed these approaches across different values of the intraclass correlation coefficient (ICC), numbers of clusters and variability in cluster sizes. Results: Unweighted and variance-weighted cluster-level analysis, mixed models with degree-of-freedom corrections, and GEE with a small-sample correction all maintained the type I error rate at or below 5% across most scenarios, whereas uncorrected approaches lead to inflated type I error rates. However, these analyses had low power (below 50% in some scenarios) when fewer than 20 clusters were randomized, with none reaching the expected 80% power. Conclusions: Small-sample corrections or variance-weighted cluster-level analyses are recommended for the analysis of continuous outcomes in CRTs with a small number of clusters. The use of these corrections should be incorporated into the sample size calculation to prevent studies from being underpowered.

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Research works printed but not linked: 4

  1. Campbell, 2014, How to Design, Analyse and Report Cluster Randomized Trials in Medicine and Health Related Research
  2. PROC MIXED, Mixed Models Theory: SAS/STAT(R) 9.2 User’s Guide
  3. Cook, Small-sample robust variance correction for generalized estimating equations for use in cluster randomized clinical trials
  4. Halekoh, 2016, Parametric Bootstrap and Kenward Roger Based Methods for Mixed Model Comparison

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Metadata

Authors
Leyrat, C., Morgan, K. E., Leurent, B., & Kahan, B. C.
Affiliations
  • Inserm (FR)
  • London School of Hygiene & Tropical Medicine (GB)
  • Queen Mary University of London (GB)
Corresponding author
Leyrat, Clémence
ORCID
Venue
International Journal of Epidemiology
Publisher
Oxford University Press (OUP)
Published
2017-08-23
Volume
47
Issue
1
Pages
321-331
ISSN
  • 0300-5771 (ISSN-L)
  • 1464-3685
DOI
10.1093/ije/dyx169
OpenAlex
W2746185216
Cited by
244
References
31
Citations by year
2026: 34 · 2025: 33 · 2024: 34 · 2023: 32 · 2022: 39 · 2021: 30 · 2020: 21 · 2019: 17 · 2018: 4
FWCI
8.02
Type
journal article
Language
en
Open access
closed
Locations
  • International Journal of Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
small number of clusters, type I error rate, statistical power, small sample correction, mixed models, generalized estimating equations, intraclass correlation coefficient, sample size determination, continuous outcomes
MeSH terms
Analysis of Variance, Computer Simulation, Data Interpretation, Statistical, Humans, Models, Statistical, Cluster Analysis, Randomized Controlled Trials as Topic, Sample Size

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