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Article: Bayesian transformation cure frailty models with multivariate failure time data.

TitleBayesian transformation cure frailty models with multivariate failure time data.
Authors
Issue Date2008
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics In Medicine, 2008, v. 27 n. 28, p. 5929-5940 How to Cite?
AbstractWe propose a class of transformation cure frailty models to accommodate a survival fraction in multivariate failure time data. Established through a general power transformation, this family of cure frailty models includes the proportional hazards and the proportional odds modeling structures as two special cases. Within the Bayesian paradigm, we obtain the joint posterior distribution and the corresponding full conditional distributions of the model parameters for the implementation of Gibbs sampling. Model selection is based on the conditional predictive ordinate statistic and deviance information criterion. As an illustration, we apply the proposed method to a real data set from dentistry.
Persistent Identifierhttp://hdl.handle.net/10722/146592
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.date.accessioned2012-05-02T08:37:15Z-
dc.date.available2012-05-02T08:37:15Z-
dc.date.issued2008en_HK
dc.identifier.citationStatistics In Medicine, 2008, v. 27 n. 28, p. 5929-5940en_HK
dc.identifier.issn0277-6715en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146592-
dc.description.abstractWe propose a class of transformation cure frailty models to accommodate a survival fraction in multivariate failure time data. Established through a general power transformation, this family of cure frailty models includes the proportional hazards and the proportional odds modeling structures as two special cases. Within the Bayesian paradigm, we obtain the joint posterior distribution and the corresponding full conditional distributions of the model parameters for the implementation of Gibbs sampling. Model selection is based on the conditional predictive ordinate statistic and deviance information criterion. As an illustration, we apply the proposed method to a real data set from dentistry.en_HK
dc.languageengen_US
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/en_HK
dc.relation.ispartofStatistics in medicineen_HK
dc.subject.meshBayes Theoremen_US
dc.subject.meshBiometry - Methodsen_US
dc.subject.meshDentistry - Statistics & Numerical Dataen_US
dc.subject.meshDisease-Free Survivalen_US
dc.subject.meshHumansen_US
dc.subject.meshLikelihood Functionsen_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshMultivariate Analysisen_US
dc.subject.meshProportional Hazards Modelsen_US
dc.subject.meshRoot Canal Therapy - Methodsen_US
dc.subject.meshTreatment Outcomeen_US
dc.titleBayesian transformation cure frailty models with multivariate failure time data.en_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.pmid18618427-
dc.identifier.scopuseid_2-s2.0-60849099675en_HK
dc.identifier.volume27en_HK
dc.identifier.issue28en_HK
dc.identifier.spage5929en_HK
dc.identifier.epage5940en_HK
dc.identifier.isiWOS:000261143200009-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK

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