Non-parametric paired two-sample tests for censored survival data incorporating longitudinal covariate information

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dc.contributor.author Messinger, Shari en_US
dc.contributor.author Murray, Susan en_US
dc.date.accessioned 2007-09-18T19:25:01Z
dc.date.available 2007-09-18T19:25:01Z
dc.date.issued 2005-01-30 en_US
dc.identifier.citation Messinger, 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.issn 0277-6715 en_US
dc.identifier.issn 1097-0258 en_US
dc.identifier.uri http://hdl.handle.net/2027.42/55815
dc.identifier.uri http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15558696&dopt=citation en_US
dc.description.abstract In 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.extent 180041 bytes
dc.format.extent 3118 bytes
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.publisher John Wiley & Sons, Ltd. en_US
dc.subject.other Mathematics and Statistics en_US
dc.title Non-parametric paired two-sample tests for censored survival data incorporating longitudinal covariate information en_US
dc.type Article en_US
dc.rights.robots IndexNoFollow en_US
dc.subject.hlbsecondlevel Medicine (General) en_US
dc.subject.hlbsecondlevel Statistics and Numeric Data en_US
dc.subject.hlbsecondlevel Public Health en_US
dc.subject.hlbtoplevel Health Sciences en_US
dc.subject.hlbtoplevel Science en_US
dc.subject.hlbtoplevel Social Sciences en_US
dc.description.peerreviewed Peer Reviewed en_US
dc.contributor.affiliationum Department 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.affiliationother Department 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.pmid 15558696 en_US
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/55815/1/1888_ftp.pdf en_US
dc.identifier.doi http://dx.doi.org/10.1002/sim.1888 en_US
dc.identifier.source Statistics in Medicine en_US
dc.owningcollname Interdisciplinary and Peer-Reviewed
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