 A Mixture Model for the Regression Analysis of Competing Risks DataMartin G. Larson and Gregg E. Dinse Journal of the Royal Statistical Society Series C, 1985, vol. 34, issue 3, 201-211 Abstract: A parametric mixture model provides a regression framework for analysing failure‐time data that are subject to censoring and multiple modes of failure. The regression context allows us to adjust for concomitant variables and to assess their effects on the joint distribution of time and type of failure. The mixing parameters correspond to the marginal probabilities of the various failure types and are modelled as logistic functions of the covariates. The hazard rate for each conditional distribution of time to failure, given type of failure, is modelled as the product of a piece‐wise exponential function of time and a log‐linear function of the covariates. An EM algorithm facilitates the maximum likelihood analysis and illuminates the contributions of the censored observations. The methods are illustrated with data from a heart transplant study and are compared with a cause‐specific hazard analysis. The proposed mixture model can also be used to analyse multivariate failure‐time data. Date: 1985 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:34:y:1985:i:3:p:201-211 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC. |
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