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Conference Paper: Bayesian transformation hazard models

TitleBayesian transformation hazard models
Authors
KeywordsAdditive hazards
Bayesian inference
Constrained parameter
CPO
DIC
Piecewise exponential distributio
Proportional hazards
Issue Date2006
PublisherInstitute of Mathematical Statistics.
Citation
Optimality: The Second Erich L. Lehmann Symposium, Beachwood, OH, 19–22 May 2004, v. 49, p. 170-182 How to Cite?
AbstractWe propose a class of transformation hazard models for rightcensored failure time data. It includes the proportional hazards model (Cox) and the additive hazards model (Lin and Ying) as special cases. Due to the requirement of a nonnegative hazard function, multidimensional parameter constraints must be imposed in the model formulation. In the Bayesian paradigm, the nonlinear parameter constraint introduces many new computational challenges. We propose a prior through a conditional-marginal specification, in which the conditional distribution is univariate, and absorbs all of the nonlinear parameter constraints. The marginal part of the prior specification is free of any constraints. This class of prior distributions allows us to easily compute the full conditionals needed for Gibbs sampling, and hence implement the Markov chain Monte Carlo algorithm in a relatively straightforward fashion. Model comparison is based on the conditional predictive ordinate and the deviance information criterion. This new class of models is illustrated with a simulation study and a real dataset from a melanoma clinical trial.
Persistent Identifierhttp://hdl.handle.net/10722/146603
ISBN

 

DC FieldValueLanguage
dc.contributor.authorYin, G-
dc.contributor.authorIbrahim, JG-
dc.date.accessioned2012-05-07T02:54:40Z-
dc.date.available2012-05-07T02:54:40Z-
dc.date.issued2006-
dc.identifier.citationOptimality: The Second Erich L. Lehmann Symposium, Beachwood, OH, 19–22 May 2004, v. 49, p. 170-182-
dc.identifier.isbn0-940600-66-9-
dc.identifier.urihttp://hdl.handle.net/10722/146603-
dc.description.abstractWe propose a class of transformation hazard models for rightcensored failure time data. It includes the proportional hazards model (Cox) and the additive hazards model (Lin and Ying) as special cases. Due to the requirement of a nonnegative hazard function, multidimensional parameter constraints must be imposed in the model formulation. In the Bayesian paradigm, the nonlinear parameter constraint introduces many new computational challenges. We propose a prior through a conditional-marginal specification, in which the conditional distribution is univariate, and absorbs all of the nonlinear parameter constraints. The marginal part of the prior specification is free of any constraints. This class of prior distributions allows us to easily compute the full conditionals needed for Gibbs sampling, and hence implement the Markov chain Monte Carlo algorithm in a relatively straightforward fashion. Model comparison is based on the conditional predictive ordinate and the deviance information criterion. This new class of models is illustrated with a simulation study and a real dataset from a melanoma clinical trial.-
dc.languageeng-
dc.publisherInstitute of Mathematical Statistics.-
dc.relation.ispartofIMS Lecture Notes - Monographs Series-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAdditive hazards-
dc.subjectBayesian inference-
dc.subjectConstrained parameter-
dc.subjectCPO-
dc.subjectDIC-
dc.subjectPiecewise exponential distributio-
dc.subjectProportional hazards-
dc.titleBayesian transformation hazard modelsen_US
dc.typeConference_Paperen_US
dc.identifier.emailYin, G: gyin@hku.hk-
dc.description.naturepostprint-
dc.identifier.doi10.1214/074921706000000446-
dc.identifier.volume49-
dc.identifier.spage170-
dc.identifier.epage182-
dc.publisher.placeUSA-

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