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Article: A general class of Bayesian survival models with zero and nonzero cure fractions

TitleA general class of Bayesian survival models with zero and nonzero cure fractions
Authors
KeywordsBayesian computation
Box-Cox transformation
Constrained parameter
Cure rate model
Gaussian quadrature
Gibbs sampling
Issue Date2005
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOM
Citation
Biometrics, 2005, v. 61 n. 2, p. 403-412+649 How to Cite?
AbstractWe propose a new class of survival models which naturally links a family of proper and improper population survival functions. The models resulting in improper survival functions are often referred to as cure rate models. This class of regression models is formulated through the Box-Cox transformation on the population hazard function and a proper density function. By adding an extra transformation parameter into the cure rate model, we are able to generate models with a zero cure rate, thus leading to a proper population survival function. A graphical illustration of the behavior and the influence of the transformation parameter on the regression model is provided. We consider a Bayesian approach which is motivated by the complexity of the model. Prior specification needs to accommodate parameter constraints due to the nonnegativity of the survival function. Moreover, the likelihood function involves a complicated integral on the survival function, which may not have an analytical closed form, and thus makes the implementation of Gibbs sampling more difficult. We propose an efficient Markov chain Monte Carlo computational scheme based on Gaussian quadrature. The proposed method is illustrated with an example involving a melanoma clinical trial.
Persistent Identifierhttp://hdl.handle.net/10722/146564
ISSN
2015 Impact Factor: 1.36
2015 SCImago Journal Rankings: 1.906
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.contributor.authorIbrahim, JGen_HK
dc.date.accessioned2012-05-02T08:37:02Z-
dc.date.available2012-05-02T08:37:02Z-
dc.date.issued2005en_HK
dc.identifier.citationBiometrics, 2005, v. 61 n. 2, p. 403-412+649en_HK
dc.identifier.issn0006-341Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/146564-
dc.description.abstractWe propose a new class of survival models which naturally links a family of proper and improper population survival functions. The models resulting in improper survival functions are often referred to as cure rate models. This class of regression models is formulated through the Box-Cox transformation on the population hazard function and a proper density function. By adding an extra transformation parameter into the cure rate model, we are able to generate models with a zero cure rate, thus leading to a proper population survival function. A graphical illustration of the behavior and the influence of the transformation parameter on the regression model is provided. We consider a Bayesian approach which is motivated by the complexity of the model. Prior specification needs to accommodate parameter constraints due to the nonnegativity of the survival function. Moreover, the likelihood function involves a complicated integral on the survival function, which may not have an analytical closed form, and thus makes the implementation of Gibbs sampling more difficult. We propose an efficient Markov chain Monte Carlo computational scheme based on Gaussian quadrature. The proposed method is illustrated with an example involving a melanoma clinical trial.en_HK
dc.languageengen_US
dc.publisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOMen_HK
dc.relation.ispartofBiometricsen_HK
dc.subjectBayesian computationen_HK
dc.subjectBox-Cox transformationen_HK
dc.subjectConstrained parameteren_HK
dc.subjectCure rate modelen_HK
dc.subjectGaussian quadratureen_HK
dc.subjectGibbs samplingen_HK
dc.subject.meshAdulten_US
dc.subject.meshAgeden_US
dc.subject.meshAged, 80 And Overen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshAnalysis Of Varianceen_US
dc.subject.meshBayes Theoremen_US
dc.subject.meshBiometry - Methodsen_US
dc.subject.meshCancer Vaccines - Therapeutic Useen_US
dc.subject.meshClinical Trials As Topicen_US
dc.subject.meshData Interpretation, Statisticalen_US
dc.subject.meshFemaleen_US
dc.subject.meshHumansen_US
dc.subject.meshInterferon-Alpha - Therapeutic Useen_US
dc.subject.meshLikelihood Functionsen_US
dc.subject.meshMaleen_US
dc.subject.meshMarkov Chainsen_US
dc.subject.meshMelanoma - Mortality - Therapyen_US
dc.subject.meshMiddle Ageden_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshMonte Carlo Methoden_US
dc.subject.meshProportional Hazards Modelsen_US
dc.subject.meshRecombinant Proteinsen_US
dc.subject.meshRegression Analysisen_US
dc.subject.meshResearch Designen_US
dc.subject.meshSurvival Analysisen_US
dc.titleA general class of Bayesian survival models with zero and nonzero cure fractionsen_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.doi10.1111/j.1541-0420.2005.00329.xen_HK
dc.identifier.pmid16011686-
dc.identifier.scopuseid_2-s2.0-20744441090en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-20744441090&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume61en_HK
dc.identifier.issue2en_HK
dc.identifier.spage403en_HK
dc.identifier.epage412+649en_HK
dc.identifier.isiWOS:000229893900009-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridIbrahim, JG=7005341361en_HK
dc.identifier.citeulike231766-

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