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dc.contributor.authorHsu, Chiu-Hsiehen_US
dc.contributor.authorTaylor, Jeremy M. G.en_US
dc.contributor.authorMurray, Susanen_US
dc.contributor.authorCommenges, Danielen_US
dc.date.accessioned2007-09-20T18:11:53Z
dc.date.available2008-04-03T18:49:37Zen_US
dc.date.issued2007-02-20en_US
dc.identifier.citationHsu, Chiu-Hsieh; Taylor, Jeremy M. G.; Murray, Susan; Commenges, Daniel (2007). "Multiple imputation for interval censored data with auxiliary variables." Statistics in Medicine 26(4): 769-781. <http://hdl.handle.net/2027.42/55943>en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/55943
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=16755528&dopt=citationen_US
dc.description.abstractWe propose a non-parametric multiple imputation scheme, NPMLE imputation, for the analysis of interval censored survival data. Features of the method are that it converts interval-censored data problems to complete data or right censored data problems to which many standard approaches can be used, and that measures of uncertainty are easily obtained. In addition to the event time of primary interest, there are frequently other auxiliary variables that are associated with the event time. For the goal of estimating the marginal survival distribution, these auxiliary variables may provide some additional information about the event time for the interval censored observations. We extend the imputation methods to incorporate information from auxiliary variables with potentially complex structures. To conduct the imputation, we use a working failure-time proportional hazards model to define an imputing risk set for each censored observation. The imputation schemes consist of using the data in the imputing risk sets to create an exact event time for each interval censored observation. In simulation studies we show that the use of multiple imputation methods can improve the efficiency of estimators and reduce the effect of missing visits when compared to simpler approaches. We apply the approach to cytomegalovirus shedding data from an AIDS clinical trial, in which CD4 count is the auxiliary variable. Copyright © 2006 John Wiley & Sons, Ltd.en_US
dc.format.extent119484 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.titleMultiple imputation for interval censored data with auxiliary variablesen_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, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, U.S.A.en_US
dc.contributor.affiliationumDepartment of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, U.S.A.en_US
dc.contributor.affiliationotherDivision of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health and Arizona Cancer Center, University of Arizona, Tucson, AZ 85724, U.S.A. ; Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health and Arizona Cancer Center, University of Arizona, Tucson, AZ 85724, U.S.A.en_US
dc.contributor.affiliationotherINSERM E0338 Biostatistics, ISPED, Bordeaux 2 University, Bordeaux 33000, Franceen_US
dc.identifier.pmid16755528en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/55943/1/2581_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/sim.2581en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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