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A Mixture Model for the Regression Analysis of Competing Risks Data
Author(s) -
Larson Martin G.,
Dinse Gregg E.
Publication year - 1985
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.2307/2347464
Subject(s) - regression analysis , statistics , regression , regression dilution , econometrics , computer science , mathematics , polynomial regression
SUMMARY 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.