Fitting a Semi-Parametric Mixture Model for Competing Risks in Survival Data

Autor:	Russell J. Bowater, Gabriel Escarela
Rok vydání:	2008
Předmět:	Statistics and Probability Hazard (logic) Logistic distribution Proportional hazards model Expectation–maximization algorithm Statistics Conditional probability distribution Mixture model Mathematics Semiparametric model Statistical hypothesis testing
Zdroj:	Communications in Statistics - Theory and Methods. 37:277-293
ISSN:	1532-415X 0361-0926
DOI:	10.1080/03610920701649134
Popis:	A model for survival analysis is studied that is relevant for samples which are subject to multiple types of failure. In comparison with a more standard approach, through the appropriate use of hazard functions and transition probabilities, the model allows for a more accurate study of cause-specific failure with regard to both the timing and type of failure. A semiparametric specification of a mixture model is employed that is able to adjust for concomitant variables and allows for the assessment of their effects on the probabilities of eventual causes of failure through a generalized logistic model, and their effects on the corresponding conditional hazard functions by employing the Cox proportional hazards model. A carefully formulated estimation procedure is presented that uses an EM algorithm based on a profile likelihood construction. The methods discussed, which could also be used for reliability analysis, are applied to a prostate cancer data set.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a1d8afebf66bac29740c91bd275db76f https://doi.org/10.1080/03610920701649134 Zobrazit plný text záznamu