Literature DB >> 26575695

A rank-sum test for clustered data when the number of subjects in a group within a cluster is informative.

Sandipan Dutta1, Somnath Datta2.   

Abstract

The Wilcoxon rank-sum test is a popular nonparametric test for comparing two independent populations (groups). In recent years, there have been renewed attempts in extending the Wilcoxon rank sum test for clustered data, one of which (Datta and Satten, 2005, Journal of the American Statistical Association 100, 908-915) addresses the issue of informative cluster size, i.e., when the outcomes and the cluster size are correlated. We are faced with a situation where the group specific marginal distribution in a cluster depends on the number of observations in that group (i.e., the intra-cluster group size). We develop a novel extension of the rank-sum test for handling this situation. We compare the performance of our test with the Datta-Satten test, as well as the naive Wilcoxon rank sum test. Using a naturally occurring simulation model of informative intra-cluster group size, we show that only our test maintains the correct size. We also compare our test with a classical signed rank test based on averages of the outcome values in each group paired by the cluster membership. While this test maintains the size, it has lower power than our test. Extensions to multiple group comparisons and the case of clusters not having samples from all groups are also discussed. We apply our test to determine whether there are differences in the attachment loss between the upper and lower teeth and between mesial and buccal sites of periodontal patients.
© 2015, The International Biometric Society.

Entities:  

Keywords:  Correlated data; Dental data; Nonparametric tests; Wilcoxon rank-sum test; Within-cluster resampling

Mesh:

Year:  2015        PMID: 26575695      PMCID: PMC4870168          DOI: 10.1111/biom.12447

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  5 in total

1.  Marginal analyses of clustered data when cluster size is informative.

Authors:  John M Williamson; Somnath Datta; Glen A Satten
Journal:  Biometrics       Date:  2003-03       Impact factor: 2.571

2.  Informative cluster sizes for subcluster-level covariates and weighted generalized estimating equations.

Authors:  Ying Huang; Brian Leroux
Journal:  Biometrics       Date:  2011-01-31       Impact factor: 2.571

3.  A signed-rank test for clustered data.

Authors:  Somnath Datta; Glen A Satten
Journal:  Biometrics       Date:  2007-10-26       Impact factor: 2.571

4.  Prevalence and risk indicators for periodontal attachment loss in a population of older community-dwelling blacks and whites.

Authors:  J D Beck; G G Koch; R G Rozier; G E Tudor
Journal:  J Periodontol       Date:  1990-08       Impact factor: 6.993

5.  Incorporation of clustering effects for the Wilcoxon rank sum test: a large-sample approach.

Authors:  Bernard Rosner; Robert J Glynn; Mei-Ling Ting Lee
Journal:  Biometrics       Date:  2003-12       Impact factor: 2.571

  5 in total
  5 in total

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Journal:  Stat Methods Med Res       Date:  2016-09-20       Impact factor: 3.021

2.  Tests for informative cluster size using a novel balanced bootstrap scheme.

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Journal:  Front Microbiol       Date:  2022-09-02       Impact factor: 6.064

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  5 in total

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