Design and analysis of group-randomized trials: a review of recent methodological developments

Murray, David M.; Varnell, Sherri P.; Blitstein, Jonathan L.

doi:10.2105/ajph.94.3.423

Cited by 677Statistical Methods in Clinical TrialsStatistical Methods and Bayesian InferenceAdvanced Causal Inference Techniques

Design and analysis of group-randomized trials: a review of recent methodological developments

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Murray, David M., Varnell, Sherri P., Blitstein, Jonathan L.·2004·American Journal of Public Health

DOI 10.2105/ajph.94.3.423PMC PMC1448268

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Abstract

We review recent developments in the design and analysis of group-randomized trials (GRTs). Regarding design, we summarize developments in estimates of intraclass correlation, power analysis, matched designs, designs involving one group per condition, and designs in which individuals are randomized to receive treatments in groups. Regarding analysis, we summarize developments in marginal and conditional models, the sandwich estimator, model-based estimators, binary data, survival analysis, randomization tests, survey methods, latent variable methods and nonlinear mixed models, time series methods, global tests for multiple endpoints, mediation effects, missing data, trial reporting, and software. We encourage investigators who conduct GRTs to become familiar with these developments and to collaborate with methodologists who can strengthen the design and analysis of their trials.

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When using GEE for small numbers of clusters per group, the empirical-sandwich estimator can lead to an inflated type I error.
“When using GEE for small numbers of clusters per group the empirical-sandwich estimator can lead to an inflated type I error [7, 10, 17, 18].”
From Evaluation of the type I error rate when using parametric bootstrap analysis of a cluster randomized controlled trial with binary outcomes and a small number of clusters (2022) · cites this paper as [10]
A small number of clusters limits the degrees of freedom available to accurately estimate the group-level statistics (i.e., the intervention effect).
“Somewhat separately, a small number of clusters, which is the case in many cRCTs, limits the degrees of freedom available to accurately estimate the group-level statistics (i.e., the intervention effect) [10].”
From Evaluation of the type I error rate when using parametric bootstrap analysis of a cluster randomized controlled trial with binary outcomes and a small number of clusters (2022) · cites this paper as [10]
D&I research usually needs multilevel data with many group-level and individual units because statistical power is driven mainly by the number of highest-level units.
“Dissemination and implementation research, because it often tests system-level strategies, generally requires multilevel data with sizeable numbers of group level (e.g., practices or organizations) as well as individuals, as the statistical power of such designs is most strongly influenced by the number units at the highest level (75, 77).”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [77]

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Authors: Murray, D. M., Varnell, S. P., & Blitstein, J. L.
Affiliations: University of Memphis (US)
ORCID: Murray, David M.
Blitstein, Jonathan L.
Venue: American Journal of Public Health
Publisher: American Public Health Association
Published: 2004-03
Volume: 94
Issue: 3
Pages: 423-432
ISSN: 0090-0036 (ISSN-L)
1541-0048
DOI: 10.2105/ajph.94.3.423
PMC: PMC1448268
OpenAlex: W2121944882
Cited by: 677
References: 133
Citations by year: 2026: 17 · 2025: 36 · 2024: 23 · 2023: 27 · 2022: 30 · 2021: 30 · 2020: 28 · 2019: 31 · 2018: 25 · 2017: 42 · 2016: 35 · 2015: 37 · 2014: 40 · 2013: 37 · 2012: 34
FWCI: 27.02
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: American Journal of Public Health (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
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: group-randomized trials, intraclass correlation coefficient, power analysis, matched design, marginal models, conditional models, sandwich estimator, binary data, survival analysis, permutation tests, survey methodology, latent variable methods, nonlinear mixed-effects models, time series methods, mediation analysis, missing data, trial reporting, statistical software
MeSH terms: Humans, Software, Time Factors, Epidemiologic Research Design, Public Health Practice, Cluster Analysis, Randomized Controlled Trials as Topic

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Design and analysis of group-randomized trials: a review of recent methodological developments

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