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Sample size calculations for the design of cluster randomized trials: A summary of methodology.

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Date
2015-05
Authors
Gao, Fei
Earnest, Arul
Matchar, David B
Campbell, Michael J
Machin, David
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Abstract
Cluster randomized trial designs are growing in popularity in, for example, cardiovascular medicine research and other clinical areas and parallel statistical developments concerned with the design and analysis of these trials have been stimulated. Nevertheless, reviews suggest that design issues associated with cluster randomized trials are often poorly appreciated and there remain inadequacies in, for example, describing how the trial size is determined and the associated results are presented. In this paper, our aim is to provide pragmatic guidance for researchers on the methods of calculating sample sizes. We focus attention on designs with the primary purpose of comparing two interventions with respect to continuous, binary, ordered categorical, incidence rate and time-to-event outcome variables. Issues of aggregate and non-aggregate cluster trials, adjustment for variation in cluster size and the effect size are detailed. The problem of establishing the anticipated magnitude of between- and within-cluster variation to enable planning values of the intra-cluster correlation coefficient and the coefficient of variation are also described. Illustrative examples of calculations of trial sizes for each endpoint type are included.
Type
Journal article
Subject
Humans
Sample Size
Health Behavior
Research Design
Community Health Services
Randomized Controlled Trials as Topic
Permalink
https://hdl.handle.net/10161/22873
Published Version (Please cite this version)
10.1016/j.cct.2015.02.011
Publication Info
Gao, Fei; Earnest, Arul; Matchar, David B; Campbell, Michael J; & Machin, David (2015). Sample size calculations for the design of cluster randomized trials: A summary of methodology. Contemporary clinical trials, 42. pp. 41-50. 10.1016/j.cct.2015.02.011. Retrieved from https://hdl.handle.net/10161/22873.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Matchar

David Bruce Matchar

Professor of Medicine
My research relates to clinical practice improvement - from the development of clinical policies to their implementation in real world clinical settings. Most recently my major content focus has been cerebrovascular disease. Other major clinical areas in which I work include the range of disabling neurological conditions, cardiovascular disease, and cancer prevention. Notable features of my work are: (1) reliance on analytic strategies such as meta-analysis, simulation, decision analy
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