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Article: Cure rate models: A unified approach

TitleCure rate models: A unified approach
Authors
KeywordsBayesian inference
Box-Cox transformation
Cure fraction
Gibbs sampling
Mixture cure model
Promotion time cure model
Issue Date2005
PublisherStatistical Society of Canada. The Journal's web site is located at http://www.mat.ulaval.ca/cjs
Citation
Canadian Journal Of Statistics, 2005, v. 33 n. 4, p. 559-570 How to Cite?
AbstractThe authors propose a novel class of cure rate models for right-censored failure time data. The class is formulated through a transformation on the unknown population survival function. It includes the mixture cure model and the promotion time cure model as two special cases. The authors propose a general form of the covariate structure which automatically satisfies an inherent parameter constraint and includes the corresponding binomial and exponential covariate structures in the two main formulations of cure models. The proposed class provides a natural link between the mixture and the promotion time cure models, and it offers a wide variety of new modelling structures as well. Within the Bayesian paradigm, a Markov chain Monte Carlo computational scheme is implemented for sampling from the full conditional distributions of the parameters. Model selection is based on the conditional predictive ordinate criterion. The use of the new class of models is illustrated with a set of real data involving a melanoma clinical trial.
Persistent Identifierhttp://hdl.handle.net/10722/146604
ISSN
2015 Impact Factor: 0.413
2015 SCImago Journal Rankings: 0.737
References

 

DC FieldValueLanguage
dc.contributor.authorGuosheng, Yen_HK
dc.contributor.authorIbrahim, JGen_HK
dc.date.accessioned2012-05-07T03:19:39Z-
dc.date.available2012-05-07T03:19:39Z-
dc.date.issued2005en_HK
dc.identifier.citationCanadian Journal Of Statistics, 2005, v. 33 n. 4, p. 559-570en_HK
dc.identifier.issn0319-5724en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146604-
dc.description.abstractThe authors propose a novel class of cure rate models for right-censored failure time data. The class is formulated through a transformation on the unknown population survival function. It includes the mixture cure model and the promotion time cure model as two special cases. The authors propose a general form of the covariate structure which automatically satisfies an inherent parameter constraint and includes the corresponding binomial and exponential covariate structures in the two main formulations of cure models. The proposed class provides a natural link between the mixture and the promotion time cure models, and it offers a wide variety of new modelling structures as well. Within the Bayesian paradigm, a Markov chain Monte Carlo computational scheme is implemented for sampling from the full conditional distributions of the parameters. Model selection is based on the conditional predictive ordinate criterion. The use of the new class of models is illustrated with a set of real data involving a melanoma clinical trial.en_HK
dc.languageeng-
dc.publisherStatistical Society of Canada. The Journal's web site is located at http://www.mat.ulaval.ca/cjsen_HK
dc.relation.ispartofCanadian Journal of Statisticsen_HK
dc.subjectBayesian inferenceen_HK
dc.subjectBox-Cox transformationen_HK
dc.subjectCure fractionen_HK
dc.subjectGibbs samplingen_HK
dc.subjectMixture cure modelen_HK
dc.subjectPromotion time cure modelen_HK
dc.titleCure rate models: A unified approachen_HK
dc.typeArticleen_HK
dc.identifier.emailGuosheng, Y: gyin@hku.hken_HK
dc.identifier.authorityGuosheng, Y=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/cjs.5550330407en_HK
dc.identifier.scopuseid_2-s2.0-33644698251en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33644698251&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume33en_HK
dc.identifier.issue4en_HK
dc.identifier.spage559en_HK
dc.identifier.epage570en_HK
dc.publisher.placeCanadaen_HK
dc.identifier.scopusauthoridGuosheng, Y=8725807500en_HK
dc.identifier.scopusauthoridIbrahim, JG=7005341361en_HK

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