title: A signed-rank test for clustered data.
abstract: 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.
authors.affiliation: Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky 40202, USA. somnath.datta@louisville.edu
authors.forename: Somnath
authors.initials: S
authors.lastname: Datta
authors.forename: Glen A
authors.initials: GA
authors.lastname: Satten
mesh_terms: D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016555:Matched-Pair Analysis; D015233:Models, Statistical