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Article: Quantile regression models with multivariate failure time data

TitleQuantile regression models with multivariate failure time data
Authors
KeywordsBootstrap
Correlation
Estimating equations
Kaplan-Meier estimator
Perturbation
Regression quantile
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. 1, p. 151-161 How to Cite?
AbstractAs an alternative to the mean regression model, the quantile regression model has been studied extensively with independent failure time data. However, due to natural or artificial clustering, it is common to encounter multivariate failure time data in biomedical research where the intracluster correlation needs to be accounted for appropriately. For right-censored correlated survival data, we investigate the quantile regression model and adapt an estimating equation approach for parameter estimation under the working independence assumption, as well as a weighted version for enhancing the efficiency. We show that the parameter estimates are consistent and asymptotically follow normal distributions. The variance estimation using asymptotic approximation involves nonparametric functional density estimation. We employ the bootstrap and perturbation resampling methods for the estimation of the variance-covariance matrix. We examine the proposed method for finite sample sizes through simulation studies, and illustrate it with data from a clinical trial on otitis media.
Persistent Identifierhttp://hdl.handle.net/10722/146558
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.authorCai, Jen_HK
dc.date.accessioned2012-05-02T08:37:00Z-
dc.date.available2012-05-02T08:37:00Z-
dc.date.issued2005en_HK
dc.identifier.citationBiometrics, 2005, v. 61 n. 1, p. 151-161en_HK
dc.identifier.issn0006-341Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/146558-
dc.description.abstractAs an alternative to the mean regression model, the quantile regression model has been studied extensively with independent failure time data. However, due to natural or artificial clustering, it is common to encounter multivariate failure time data in biomedical research where the intracluster correlation needs to be accounted for appropriately. For right-censored correlated survival data, we investigate the quantile regression model and adapt an estimating equation approach for parameter estimation under the working independence assumption, as well as a weighted version for enhancing the efficiency. We show that the parameter estimates are consistent and asymptotically follow normal distributions. The variance estimation using asymptotic approximation involves nonparametric functional density estimation. We employ the bootstrap and perturbation resampling methods for the estimation of the variance-covariance matrix. We examine the proposed method for finite sample sizes through simulation studies, and illustrate it with data from a clinical trial on otitis media.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.subjectBootstrapen_HK
dc.subjectCorrelationen_HK
dc.subjectEstimating equationsen_HK
dc.subjectKaplan-Meier estimatoren_HK
dc.subjectPerturbationen_HK
dc.subjectRegression quantileen_HK
dc.subject.meshBiometryen_US
dc.subject.meshChilden_US
dc.subject.meshChild, Preschoolen_US
dc.subject.meshClinical Trials As Topic - Methodsen_US
dc.subject.meshCluster Analysisen_US
dc.subject.meshGraft Survivalen_US
dc.subject.meshHumansen_US
dc.subject.meshInfanten_US
dc.subject.meshMultivariate Analysisen_US
dc.subject.meshMyringoplastyen_US
dc.subject.meshOtitis Media - Surgeryen_US
dc.subject.meshProbabilityen_US
dc.subject.meshRegression Analysisen_US
dc.subject.meshSample Sizeen_US
dc.subject.meshTime Factorsen_US
dc.subject.meshTreatment Failureen_US
dc.titleQuantile regression models with multivariate failure time dataen_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.0006-341X.2005.030815.xen_HK
dc.identifier.pmid15737088-
dc.identifier.scopuseid_2-s2.0-15044342852en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-15044342852&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume61en_HK
dc.identifier.issue1en_HK
dc.identifier.spage151en_HK
dc.identifier.epage161en_HK
dc.identifier.isiWOS:000227576600017-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridCai, J=7403153136en_HK
dc.identifier.citeulike110092-

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