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Non-parametric paired two-sample tests for censored survival data incorporating longitudinal covariate information
dc.contributor.authorMessinger, Sharien_US
dc.contributor.authorMurray, Susanen_US
dc.date.accessioned2007-09-18T19:25:01Z
dc.date.available2007-09-18T19:25:01Z
dc.date.issued2005-01-30en_US
dc.identifier.citationMessinger, Shari; Murray, Susan (2005)."Non-parametric paired two-sample tests for censored survival data incorporating longitudinal covariate information." Statistics in Medicine 24(2): 301-318. <http://hdl.handle.net/2027.42/55815>en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/55815
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15558696&dopt=citationen_US
dc.description.abstractIn this manuscript, we present non-parametric two-sample tests for paired censored survival data incorporating longitudinal covariate information. These tests take advantage of information collected at baseline and post-baseline to provide efficiency gains when censoring is uninformative. Additionally, these methods adjust for potential bias from informative censoring that is captured by the baseline and longitudinal covariates. Finite sample properties are investigated with simulation, and we illustrate methodology with an example from the Early Treatment Diabetic Retinopathy Study. Copyright © 2004 John Wiley & Sons, Ltd.en_US
dc.format.extent180041 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherJohn Wiley & Sons, Ltd.en_US
dc.subject.otherMathematics and Statisticsen_US
dc.titleNon-parametric paired two-sample tests for censored survival data incorporating longitudinal covariate informationen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, U.S.A. ; ScD. ; Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, U.S.A.en_US
dc.contributor.affiliationotherDepartment of Epidemiology and Public Health, University of Miami School of Medicine, 1801 NW 9th Avenue 3rd floor, Miami, FL 33136, U.S.A. ; PhD.en_US
dc.identifier.pmid15558696en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/55815/1/1888_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/sim.1888en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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