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Empirical power and sample size calculations for cluster-randomized and cluster-randomized crossover studies
Nicholas G Reich1, Jessica A Myers, Daniel Obeng
1Division of Biostatistics and Epidemiology, University of Massachusetts, Amherst, Massachusetts, United States of America.
Plos One
|May 5, 2012
Summary
Estimating statistical power for cluster-randomized trials is crucial. A new R package, clusterPower, offers a universal simulation framework for power calculations in cluster-randomized and cluster-randomized crossover designs.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Health Services Research
Background:
- Cluster-randomized designs are increasingly used in research.
- Cluster-randomized crossover trials offer potential efficiency gains.
- Established standards for cluster-randomized crossover trials are lacking.
Purpose of the Study:
- To present a general simulation framework for estimating statistical power in cluster-randomized and cluster-randomized crossover trials.
- To introduce the clusterPower software package for R to implement this framework.
Main Methods:
- Developed a simulation-based framework for power estimation.
- Implemented the framework in the R package clusterPower.
- Provided examples of software application in practice.
Main Results:
- The clusterPower package provides a universal method for calculating power for both cluster-randomized and cluster-randomized crossover trials.
- The simulation framework is customizable for different data analysis methods.
Conclusions:
- The clusterPower package can aid in the design of future cluster-randomized and cluster-randomized crossover studies.
- Further research is needed to standardize methodology for cluster-randomized crossover studies.
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