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Article: Modeling multivariate survival data by a semiparametric random effects proportional odds model

TitleModeling multivariate survival data by a semiparametric random effects proportional odds model
Authors
KeywordsGHK simulator
Monte Carlo method
Random effects
Rank invariant transformation
Semiparametric proportional odds model
Issue Date2002
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOM
Citation
Biometrics, 2002, v. 58 n. 2, p. 316-323 How to Cite?
AbstractIn this article, the focus is on the analysis of multivariate survival time data with various types of dependence structures. Examples of multivariate survival data include clustered data and repeated measurements from the same subject, such as the interrecurrence times of cancer tumors. A random effect semiparametric proportional odds model is proposed as an alternative to the proportional hazards model. The distribution of the random effects is assumed to be multivariate normal and the random effect is assumed to act additively to the baseline log-odds function. This class of models, which includes the usual shared random effects model, the additive variance components model, and the dynamic random effects model as special cases, is highly flexible and is capable of modeling a wide range of multivariate survival data. A unified estimation procedure is proposed to estimate the regression and dependence parameters simultaneously by means of a marginal-likelihood approach. Unlike the fully parametric case, the regression parameter estimate is not sensitive to the choice of correlation structure of the random effects. The marginal likelihood is approximated by the Monte Carlo method. Simulation studies are carried out to investigate the performance of the proposed method. The proposed method is applied to two well-known data sets, including clustered data and recurrent event times data.
Persistent Identifierhttp://hdl.handle.net/10722/82834
ISSN
2015 Impact Factor: 1.36
2015 SCImago Journal Rankings: 1.906
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KFen_HK
dc.contributor.authorLee, YWen_HK
dc.contributor.authorLeung, TLen_HK
dc.date.accessioned2010-09-06T08:33:56Z-
dc.date.available2010-09-06T08:33:56Z-
dc.date.issued2002en_HK
dc.identifier.citationBiometrics, 2002, v. 58 n. 2, p. 316-323en_HK
dc.identifier.issn0006-341Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/82834-
dc.description.abstractIn this article, the focus is on the analysis of multivariate survival time data with various types of dependence structures. Examples of multivariate survival data include clustered data and repeated measurements from the same subject, such as the interrecurrence times of cancer tumors. A random effect semiparametric proportional odds model is proposed as an alternative to the proportional hazards model. The distribution of the random effects is assumed to be multivariate normal and the random effect is assumed to act additively to the baseline log-odds function. This class of models, which includes the usual shared random effects model, the additive variance components model, and the dynamic random effects model as special cases, is highly flexible and is capable of modeling a wide range of multivariate survival data. A unified estimation procedure is proposed to estimate the regression and dependence parameters simultaneously by means of a marginal-likelihood approach. Unlike the fully parametric case, the regression parameter estimate is not sensitive to the choice of correlation structure of the random effects. The marginal likelihood is approximated by the Monte Carlo method. Simulation studies are carried out to investigate the performance of the proposed method. The proposed method is applied to two well-known data sets, including clustered data and recurrent event times data.en_HK
dc.languageengen_HK
dc.publisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOMen_HK
dc.relation.ispartofBiometricsen_HK
dc.rightsBiometrics. Copyright © Blackwell Publishing Ltd.en_HK
dc.subjectGHK simulatoren_HK
dc.subjectMonte Carlo methoden_HK
dc.subjectRandom effectsen_HK
dc.subjectRank invariant transformationen_HK
dc.subjectSemiparametric proportional odds modelen_HK
dc.titleModeling multivariate survival data by a semiparametric random effects proportional odds modelen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-341X&volume=58&issue=2&spage=316&epage=323&date=2002&atitle=Modeling+multivariate+survival+data+by+a+semiparametric+random+effects+proportional+odds+modelen_HK
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_HK
dc.identifier.authorityLam, KF=rp00718en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.pmid12071404-
dc.identifier.scopuseid_2-s2.0-0035989810en_HK
dc.identifier.hkuros67530en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035989810&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume58en_HK
dc.identifier.issue2en_HK
dc.identifier.spage316en_HK
dc.identifier.epage323en_HK
dc.identifier.isiWOS:000176157000007-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridLee, YW=8948421100en_HK
dc.identifier.scopusauthoridLeung, TL=7202110906en_HK

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