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A comparison of the statistical power of different methods for the analysis of cluster randomization trials with binary outcomes
Author(s) -
Austin Peter C.
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2813
Subject(s) - statistics , wilcoxon signed rank test , statistical power , mathematics , generalized estimating equation , cluster randomised controlled trial , statistical hypothesis testing , randomization , intraclass correlation , type i and type ii errors , restricted randomization , randomized controlled trial , sample size determination , resampling , random effects model , meta analysis , medicine , mann–whitney u test , surgery , psychometrics
Cluster randomization trials are randomized controlled trials (RCTs) in which intact clusters of subjects are randomized to either the intervention or to the control. Cluster randomization trials require different statistical methods of analysis than do conventional randomized controlled trials due to the potential presence of within‐cluster homogeneity in responses. A variety of statistical methods have been proposed in the literature for the analysis of cluster randomization trials with binary outcomes. However, little is known about the relative statistical power of these methods to detect a statistically significant intervention effect. We conducted a series of Monte Carlo simulations to examine the statistical power of three methods that compare cluster‐specific response rates between arms of the trial: the t ‐test, the Wilcoxon rank sum test, and the permutation test; and three methods that compare subject‐level response rates: an adjusted chi‐square test, a logistic‐normal random effects model, and a generalized estimating equations (GEE) method. In our simulations we allowed the number of clusters, the number of subjects per cluster, the intraclass correlation coefficient and the magnitude of the intervention effect to vary. We demonstrated that the GEE approach tended to have the highest power for detecting a statistically significant intervention effect. However, in most of the 240 scenarios examined, the differences between the competing statistical methods were negligible. The largest mean difference in power between any two different statistical methods across the 240 scenarios was 0.02. The largest observed difference in power between two different statistical methods across the 240 scenarios and 15 pair‐wise comparisons of methods was 0.14. Copyright © 2007 John Wiley & Sons, Ltd.