Non-parametric Maximum-Likelihood Estimation in a Semiparametric Mixture Model for Competing-Risks Data

Authors: CHANG, I-SHOU¹; HSIUNG, CHAO A.²; WEN, CHI-CHUNG³; WU, YUH-JENN⁴; YANG, CHE-CHI⁵

Source: Scandinavian Journal of Statistics, Volume 34, Number 4, December 2007 , pp. 870-895(26)

Buy & download fulltext article:

Price: $48.00 plus tax (Refund Policy)

Abstract:

This paper describes our studies on non-parametric maximum-likelihood estimators in a semiparametric mixture model for competing-risks data, in which proportional hazards models are specified for failure time models conditional on cause and a multinomial model is specified for the marginal distribution of cause conditional on covariates. We provide a verifiable identifiability condition and, based on it, establish an asymptotic profile likelihood theory for this model. We also provide efficient algorithms for the computation of the non-parametric maximum-likelihood estimate and its asymptotic variance. The success of this method is demonstrated in simulation studies and in the analysis of Taiwan severe acute respiratory syndrome data.

Keywords: case fatality rate; competing-risks problems; self-consistency equation; severe acute respiratory syndrome

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-9469.2007.00567.x

Affiliations: 1: Institute of Cancer Research and Division of Biostatistics and Bioinformatics, National Health Research Institutes, Taiwan 2: Division of Biostatistics and Bioinformatics, National Health Research Institutes, Taiwan 3: Department of Mathematics, Tamkang University 4: Department of Applied Mathematics, Chung Yuan Christian University 5: Department of Information Management, Lunghwa University of Science and Technology

Publication date: 2007-12-01