A signed-rank test for clustered data.

Somnath Datta, Glen A Satten
Author Information
  1. Somnath Datta: Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky 40202, USA. somnath.datta@louisville.edu

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.

MeSH Term

Biometry
Cluster Analysis
Computer Simulation
Data Interpretation, Statistical
Matched-Pair Analysis
Models, Statistical

Word Cloud

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