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Article: Bayesian frailty models based on box-cox transformed hazards

TitleBayesian frailty models based on box-cox transformed hazards
Authors
KeywordsAdditive hazards
Bayesian inference
Box-Cox transformation
Constrained parameter
Frailty model
Gibbs sampling
Proportional hazards
Issue Date2005
PublisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/
Citation
Statistica Sinica, 2005, v. 15 n. 3, p. 781-794 How to Cite?
AbstractDue to natural or artificial clustering, multivariate failure time data often arise in biomedical research. To account for the intracluster correlation, we propose a novel class of frailty models by imposing the Box-Cox transformation on the hazard functions. This class of models generalizes the relationships between the baseline hazard and the hazard functions, which includes the proportional and the additive hazards frailty models as two special cases. Since hazards cannot be negative, complex multidimensional nonlinear parameter constraints must be imposed in the model formulation. To facilitate a tractable computational algorithm, the joint priors are constructed through a conditional-marginal specification. The conditional distribution of the prior specification is univariate and absorbs the parameter constraints, while the marginal part is free of constraints. We propose a Markov chain Monte Carlo (MCMC) computational scheme for sampling from the posterior distribution of the parameters. We derive an MCMC approximation for the conditional predictive ordinate to assess model adequacy, and illustrate the proposed method with a dataset.
Persistent Identifierhttp://hdl.handle.net/10722/146571
ISSN
2015 Impact Factor: 0.838
2015 SCImago Journal Rankings: 2.292
References

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.contributor.authorIbrahim, JGen_HK
dc.date.accessioned2012-05-02T08:37:05Z-
dc.date.available2012-05-02T08:37:05Z-
dc.date.issued2005en_HK
dc.identifier.citationStatistica Sinica, 2005, v. 15 n. 3, p. 781-794en_HK
dc.identifier.issn1017-0405en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146571-
dc.description.abstractDue to natural or artificial clustering, multivariate failure time data often arise in biomedical research. To account for the intracluster correlation, we propose a novel class of frailty models by imposing the Box-Cox transformation on the hazard functions. This class of models generalizes the relationships between the baseline hazard and the hazard functions, which includes the proportional and the additive hazards frailty models as two special cases. Since hazards cannot be negative, complex multidimensional nonlinear parameter constraints must be imposed in the model formulation. To facilitate a tractable computational algorithm, the joint priors are constructed through a conditional-marginal specification. The conditional distribution of the prior specification is univariate and absorbs the parameter constraints, while the marginal part is free of constraints. We propose a Markov chain Monte Carlo (MCMC) computational scheme for sampling from the posterior distribution of the parameters. We derive an MCMC approximation for the conditional predictive ordinate to assess model adequacy, and illustrate the proposed method with a dataset.en_HK
dc.languageengen_US
dc.publisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/en_HK
dc.relation.ispartofStatistica Sinicaen_HK
dc.subjectAdditive hazardsen_HK
dc.subjectBayesian inferenceen_HK
dc.subjectBox-Cox transformationen_HK
dc.subjectConstrained parameteren_HK
dc.subjectFrailty modelen_HK
dc.subjectGibbs samplingen_HK
dc.subjectProportional hazardsen_HK
dc.titleBayesian frailty models based on box-cox transformed hazardsen_HK
dc.typeArticleen_HK
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-27144446701en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-27144446701&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume15en_HK
dc.identifier.issue3en_HK
dc.identifier.spage781en_HK
dc.identifier.epage794en_HK
dc.publisher.placeTaiwan, Republic of Chinaen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridIbrahim, JG=7005341361en_HK

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