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A Frailty Model for Informative Censoring

dc.contributor.authorHuang, Xuelinen_US
dc.contributor.authorWolfe, Robert A.en_US
dc.date.accessioned2010-04-01T15:05:11Z
dc.date.available2010-04-01T15:05:11Z
dc.date.issued2002-09en_US
dc.identifier.citationHuang, Xuelin; Wolfe, Robert A. (2002). "A Frailty Model for Informative Censoring." Biometrics 58(3): 510-520. <http://hdl.handle.net/2027.42/65550>en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.issn1541-0420en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/65550
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=12229985&dopt=citationen_US
dc.description.abstractTo account for the correlation between failure and censoring, we propose a new frailty model for clustered data. In this model, the risk to be censored is affected by the risk of failure. This model allows flexibility in the direction and degree of dependence between failure and censoring. It includes the traditional frailty model as a special case. It allows censoring by some causes to be analyzed as informative while treating censoring by other causes as noninformative. It can also analyze data for competing risks. To fit the model, the EM algorithm is used with Markov chain Monte Carlo simulations in the E-steps. Simulation studies and analysis of data for kidney disease patients are provided. Consequences of incorrectly assuming noninformative censoring are investigated.en_US
dc.format.extent990334 bytes
dc.format.extent3110 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishing Ltden_US
dc.rightsThe International Biometric Society, 2002en_US
dc.subject.otherClustered Dataen_US
dc.subject.otherCompeting Risksen_US
dc.subject.otherDependent Censoringen_US
dc.subject.otherEM Algorithmen_US
dc.subject.otherSurvival Analysisen_US
dc.titleA Frailty Model for Informative Censoringen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109–2029, U.S.A.en_US
dc.identifier.pmid12229985en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/65550/1/j.0006-341X.2002.00510.x.pdf
dc.identifier.doi10.1111/j.0006-341X.2002.00510.xen_US
dc.identifier.sourceBiometricsen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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