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VOL. 54 | 2007 Non- and semi-parametric analysis of failure time data with missing failure indicators
Irene Gijbels, Danyu Lin, Zhiliang Ying

Editor(s) Regina Liu, William Strawderman, Cun-Hui Zhang


A class of estimating functions is introduced for the regression parameter of the Cox proportional hazards model to allow unknown failure statuses on some study subjects. The consistency and asymptotic normality of the resulting estimators are established under mild conditions. An adaptive estimator which achieves the minimum variance-covariance bound of the class is constructed. Numerical studies demonstrate that the asymptotic approximations are adequate for practical use and that the efficiency gain of the adaptive estimator over the complete-case analysis can be quite substantial. Similar methods are also developed for the nonparametric estimation of the survival function of a homogeneous population and for the estimation of the cumulative baseline hazard function under the Cox model.


Published: 1 January 2007
First available in Project Euclid: 4 December 2007

MathSciNet: MR2459190

Digital Object Identifier: 10.1214/074921707000000166

Primary: 62J99
Secondary: 62F12 , 62G05

Keywords: cause of death , Censoring , competing risks , counting process , Cox model , cumulative hazard function , failure type , incomplete data , Kaplan-Meier estimator , partial likelihood , proportional hazards , regression , survival data

Rights: Copyright © 2007, Institute of Mathematical Statistics


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