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Robust Testing of Paired Outcomes Incorporating Covariate Effects in Clustered Data with Informative Cluster Size

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  • Sandipan Dutta

    (Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA 23529, USA)

Abstract

Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a cluster size) is correlated to the paired outcomes or the paired differences. There have been some attempts to develop robust rank-based tests for comparing paired outcomes in such complex clustered data. Most of these existing rank tests developed for paired outcomes in clustered data compare the marginal distributions in a pair and ignore any covariate effect on the outcomes. However, when potentially important covariate data is available in observational studies, ignoring these covariate effects on the outcomes can result in a flawed inference. In this article, using rank based weighted estimating equations, we propose a robust procedure for covariate effect adjusted comparison of paired outcomes in a clustered data that can also address the issue of informative cluster size. Through simulated scenarios and real-life neuroimaging data, we demonstrate the importance of considering covariate effects during paired testing and robust performances of our proposed method in covariate adjusted paired comparisons in complex clustered data settings.

Suggested Citation

  • Sandipan Dutta, 2022. "Robust Testing of Paired Outcomes Incorporating Covariate Effects in Clustered Data with Informative Cluster Size," Stats, MDPI, vol. 5(4), pages 1-13, December.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:4:p:80-1333:d:1003629
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    References listed on IDEAS

    as
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    2. Somnath Datta & Jaakko Nevalainen & Hannu Oja, 2012. "A general class of signed-rank tests for clustered data when the cluster size is potentially informative," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 797-808.
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    4. Bernard Rosner & Robert J. Glynn & Mei-Ling Ting Lee, 2003. "Incorporation of Clustering Effects for the Wilcoxon Rank Sum Test: A Large-Sample Approach," Biometrics, The International Biometric Society, vol. 59(4), pages 1089-1098, December.
    5. Sandipan Dutta & Somnath Datta, 2016. "A rank-sum test for clustered data when the number of subjects in a group within a cluster is informative," Biometrics, The International Biometric Society, vol. 72(2), pages 432-440, June.
    6. Zhang, Xinyan & Sun, Jianguo, 2010. "Regression analysis of clustered interval-censored failure time data with informative cluster size," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1817-1823, July.
    7. Bernard Rosner & Robert J. Glynn & Mei-Ling T. Lee, 2006. "The Wilcoxon Signed Rank Test for Paired Comparisons of Clustered Data," Biometrics, The International Biometric Society, vol. 62(1), pages 185-192, March.
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