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The Effect of Cluster Size Variability on Statistical Power in Cluster-Randomized Trials
  • Stephen A. Lauer, University of Massachusetts Amherst
  • Ken P. Kleinman, Harvard Medical School/Harvard Pilgrim Health Care Institute
  • Nicholas G. Reich, University of Massachusetts Amherst
Publication Date
The frequency of cluster-randomized trials (CRTs) in peer-reviewed literature has increased exponentially over the past two decades. CRTs are a valuable tool for studying interventions that cannot be effectively implemented or randomized at the individual level. However, some aspects of the design and analysis of data from CRTs are more complex than those for individually randomized controlled trials. One of the key components to designing a successful CRT is calculating the proper sample size (i.e. number of clusters) needed to attain an acceptable level of statistical power. In order to do this, a researcher must make assumptions about the value of several variables, including a fixed mean cluster size. In practice, cluster size can often vary dramatically. Few studies account for the effect of cluster size variation when assessing the statistical power for a given trial. We conducted a simulation study to investigate how the statistical power of CRTs changes with variable cluster sizes. In general, we observed that increases in cluster size variability lead to a decrease in power.
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Creative Commons Attribution 4.0
UMass Amherst Open Access Policy
This project was funded by the ResPECT study (, ID: NCT01249625) through an interagency agreement between the Centers for Disease Control and the United States Department of Veterans Affairs (CDC IAA 09FED905876). UMass SOAR Fund.
Citation Information
Stephen A. Lauer, Ken P. Kleinman and Nicholas G. Reich. "The Effect of Cluster Size Variability on Statistical Power in Cluster-Randomized Trials" PLoS ONE Vol. 10 Iss. 4 (2015)
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