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Power and Sample Size Estimation for the Clustered Wilcoxon Test
Author(s) -
Rosner Bernard,
Glynn Robert J.
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01488.x
Subject(s) - wilcoxon signed rank test , statistics , sample size determination , estimation , test (biology) , mathematics , computer science , power (physics) , biology , engineering , mann–whitney u test , paleontology , physics , systems engineering , quantum mechanics
Summary The Wilcoxon rank sum test is widely used for two‐group comparisons of nonnormal data. An assumption of this test is independence of sampling units both within and between groups, which will be violated in the clustered data setting such as in ophthalmological clinical trials, where the unit of randomization is the subject, but the unit of analysis is the individual eye. For this purpose, we have proposed the clustered Wilcoxon test to account for clustering among multiple subunits within the same cluster (Rosner, Glynn, and Lee, 2003, Biometrics 59 , 1089–1098; 2006, Biometrics 62 , 1251–1259). However, power estimation is needed to plan studies that use this analytic approach. We have recently published methods for estimating power and sample size for the ordinary Wilcoxon rank sum test (Rosner and Glynn, 2009, Biometrics 65 , 188–197). In this article we present extensions of this approach to estimate power for the clustered Wilcoxon test. Simulation studies show a good agreement between estimated and empirical power. These methods are illustrated with examples from randomized trials in ophthalmology. Enhanced power is achieved with use of the subunit as the unit of analysis instead of the cluster using the ordinary Wilcoxon rank sum test.