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A signed-rank test for clustered data.

Datta S, Satten GA

Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky 40202, USA. somnath.datta@louisville.edu

We consider the problem of comparing two outcome measures when the pairs are clustered. Using the general principle of within-cluster resampling, we obtain a novel signed-rank test for clustered paired data. We show by a simple informative cluster size simulation model that only our test maintains the correct size under a null hypothesis of marginal symmetry compared to four other existing signed rank tests; further, our test has adequate power when cluster size is noninformative. In general, cluster size is informative if the distribution of pair-wise differences within a cluster depends on the cluster size. An application of our method to testing radiation toxicity trend is presented.

Published 16 May 2008 in Biometrics, 64(2): 501-7.
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