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Biometrika 2003 90(3):732-740; doi:10.1093/biomet/90.3.732
© 2003 by Biometrika Trust
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Miscellanea

Rank-based regression with repeated measurements data

Sin-Ho Jung1 and Zhiliang Ying2

1 Department of Biostatistics and Bioinformatics, Duke University, DUMC Box 3627, Durham, North Carolina 27710, U.S.Ajung0005{at}surgerytrials.duke.edu 2 Department of Statistics, MC 4403, Columbia University, 2990 Broadway, New York, New York 10027, U.S.A.zying{at}stat.columbia.edu

A rank-based regression method is proposed for repeated measurements data.It is a generalisation of the classical Wilcoxon–Mann–Whitney rank statistic for independent observations. The method is valid under a weak condition on the error terms that can accommodate certain heteroscedasticity and within-subject dependency. The asymptotic normality of the proposed estimator is proved using empirical process theory. A variance estimator, shown to be consistent, is also constructed. The proposed method is illustrated using data from a clinical trial on treating labour pain. Robustness and efficiency of the estimator is demonstrated in simulation studies.

Key Words: Completely random missing; Dependent observations; Empirical process; Estimating equation; Heteroscedasticity; Linear regression; Wilcoxon rank statistic

Received May 2002. Revised March 2003


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