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Hierarchical likelihood inference on clustered competing risks data
Author(s) -
Christian Nicholas J.,
Ha Il Do,
Jeong JongHyeon
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6628
Subject(s) - bivariate analysis , estimator , statistics , econometrics , proportional hazards model , random effects model , inference , computer science , hierarchical database model , likelihood function , hazard , maximum likelihood , mathematics , data mining , artificial intelligence , medicine , meta analysis , chemistry , organic chemistry
The frailty model, an extension of the proportional hazards model, is often used to model clustered survival data. However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events of interest and competing events could be different but correlated. In this paper, the hierarchical likelihood method is proposed to infer the cause‐specific hazard frailty model for clustered competing risks data. The hierarchical likelihood incorporates fixed effects as well as random effects into an extended likelihood function, so that the method does not require intensive numerical methods to find the marginal distribution. Simulation studies are performed to assess the behavior of the estimators for the regression coefficients and the correlation structure among the bivariate frailty distribution for competing events. The proposed method is illustrated with a breast cancer dataset. Copyright © 2015 John Wiley & Sons, Ltd.