Multivariate failure times regression with a continuous auxiliary covariate

JOURNAL OF MULTIVARIATE ANALYSIS

卷:101

Article

Liu, Yanyan¹  Wu, Yuanshan^1,2  Zhou, Haibo² 

[1] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Hubei, Peoples R China

[2] Univ N Carolina, Dept Biostat, Chapel Hill, NC 27599 USA

关键词: Multivariate failure times;    Auxiliary covariate;    Pseudo-partial likelihood;    Kernel smoothing;    Validation sample;   

DOI  :  10.1016/j.jmva.2009.09.008

来源: Elsevier

【 摘 要 】

How to take advantage of the available auxiliary covariate information when the primary covariate of interest is not measured is a frequently encountered question in biomedical study. In this paper, we consider the multivariate failure times regression analysis in which the primary covariate is assessed only in a validation set, but a continuous auxiliary covariate for it is available for all subjects in the study cohort. Under the frame of marginal hazard model, we propose to estimate the induced relative risk function in the non-validation set through kernel smoothing method and then obtain an estimated pseudo-partial likelihood function. The proposed estimator which maximizes the estimated pseudo-partial likelihood is shown to be consistent and asymptotically normal. We also give an estimator of the marginal cumulative baseline hazard function. Simulation studies are conducted to evaluate the finite sample performance of our proposed estimator. The proposed method is illustrated by analyzing a heart disease data from the Study of Left Ventricular Dysfunction (SOLVD). (C) 2009 Elsevier Inc. All rights reserved.

【 授权许可】

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