Nonparametric estimation of competing risks models with covariates

Jean-David Fermanian

Journal of Multivariate Analysis, 2003, vol. 85, issue 1, 156-191

Abstract: In competing risks model, several failure times arise potentially. The smallest failure time and its index only are observed. Without specific assumptions, the joint or even the marginal distribution functions of the underlying failure times are not identifiable (A. Tsiatis, Proc. Natl. Acad. Sci. USA 72 (1975) 20). Nonetheless, if each individual is characterized by a "sufficiently informative" set of covariates, these distributions are identifiable under some conditions of regularity (J.J. Heckman and B. Honoré, Biometrika 76 (1989) 325). In this paper, nonparametric kernel estimators of the joint distribution function of failure times conditional on the covariates are proposed. Their weak and strong consistency are discussed.

Keywords: Competing; risks; Nonparametric; estimation; Covariates; Kernel; method (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (3)

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