Rank regression for accelerated failure time model with clustered and censored data
AbstractFor clustered survival data, the traditional Gehan-type estimator is asymptotically equivalent to using only the between-cluster ranks, and the within-cluster ranks are ignored. The contribution of this paper is two fold, (i) incorporating within-cluster ranks in censored data analysis, and (ii) applying the induced smoothing of Brown and Wang (2005, Biometrika) for computational convenience. Asymptotic properties of the resulting estimating functions are given. We also carry out numerical studies to assess the performance of the proposed approach and conclude that the proposed approach can lead to much improved estimators when strong clustering effects exist. A dataset from a litter-matched tumorigenesis experiment is used for illustration.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 55 (2011)
Issue (Month): 7 (July)
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Web page: http://www.elsevier.com/locate/csda
Clustered data Covariance matrix Gehan-type weight function Induced smoothing Rank estimation Survival data;
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