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- Publisher Website: 10.1111/j.1541-0420.2005.00329.x
- Scopus: eid_2-s2.0-20744441090
- PMID: 16011686
- WOS: WOS:000229893900009
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Article: A general class of Bayesian survival models with zero and nonzero cure fractions
Title | A general class of Bayesian survival models with zero and nonzero cure fractions |
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Authors | |
Keywords | Bayesian computation Box-Cox transformation Constrained parameter Cure rate model Gaussian quadrature Gibbs sampling |
Issue Date | 2005 |
Publisher | Blackwell 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? |
Abstract | We 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 Identifier | http://hdl.handle.net/10722/146564 |
ISSN | 2023 Impact Factor: 1.4 2023 SCImago Journal Rankings: 1.480 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yin, G | en_HK |
dc.contributor.author | Ibrahim, JG | en_HK |
dc.date.accessioned | 2012-05-02T08:37:02Z | - |
dc.date.available | 2012-05-02T08:37:02Z | - |
dc.date.issued | 2005 | en_HK |
dc.identifier.citation | Biometrics, 2005, v. 61 n. 2, p. 403-412+649 | en_HK |
dc.identifier.issn | 0006-341X | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/146564 | - |
dc.description.abstract | We 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.language | eng | en_US |
dc.publisher | Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOM | en_HK |
dc.relation.ispartof | Biometrics | en_HK |
dc.subject | Bayesian computation | en_HK |
dc.subject | Box-Cox transformation | en_HK |
dc.subject | Constrained parameter | en_HK |
dc.subject | Cure rate model | en_HK |
dc.subject | Gaussian quadrature | en_HK |
dc.subject | Gibbs sampling | en_HK |
dc.subject.mesh | Adult | en_US |
dc.subject.mesh | Aged | en_US |
dc.subject.mesh | Aged, 80 And Over | en_US |
dc.subject.mesh | Algorithms | en_US |
dc.subject.mesh | Analysis Of Variance | en_US |
dc.subject.mesh | Bayes Theorem | en_US |
dc.subject.mesh | Biometry - Methods | en_US |
dc.subject.mesh | Cancer Vaccines - Therapeutic Use | en_US |
dc.subject.mesh | Clinical Trials As Topic | en_US |
dc.subject.mesh | Data Interpretation, Statistical | en_US |
dc.subject.mesh | Female | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | Interferon-Alpha - Therapeutic Use | en_US |
dc.subject.mesh | Likelihood Functions | en_US |
dc.subject.mesh | Male | en_US |
dc.subject.mesh | Markov Chains | en_US |
dc.subject.mesh | Melanoma - Mortality - Therapy | en_US |
dc.subject.mesh | Middle Aged | en_US |
dc.subject.mesh | Models, Statistical | en_US |
dc.subject.mesh | Monte Carlo Method | en_US |
dc.subject.mesh | Proportional Hazards Models | en_US |
dc.subject.mesh | Recombinant Proteins | en_US |
dc.subject.mesh | Regression Analysis | en_US |
dc.subject.mesh | Research Design | en_US |
dc.subject.mesh | Survival Analysis | en_US |
dc.title | A general class of Bayesian survival models with zero and nonzero cure fractions | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Yin, G: gyin@hku.hk | en_HK |
dc.identifier.authority | Yin, G=rp00831 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1111/j.1541-0420.2005.00329.x | en_HK |
dc.identifier.pmid | 16011686 | - |
dc.identifier.scopus | eid_2-s2.0-20744441090 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-20744441090&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 61 | en_HK |
dc.identifier.issue | 2 | en_HK |
dc.identifier.spage | 403 | en_HK |
dc.identifier.epage | 412+649 | en_HK |
dc.identifier.isi | WOS:000229893900009 | - |
dc.publisher.place | United Kingdom | en_HK |
dc.identifier.scopusauthorid | Yin, G=8725807500 | en_HK |
dc.identifier.scopusauthorid | Ibrahim, JG=7005341361 | en_HK |
dc.identifier.citeulike | 231766 | - |
dc.identifier.issnl | 0006-341X | - |