Multiple imputation for interval censored data with auxiliary variables

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dc.contributor.author Hsu, Chiu-Hsieh en_US
dc.contributor.author Taylor, Jeremy M. G. en_US
dc.contributor.author Murray, Susan en_US
dc.contributor.author Commenges, Daniel en_US
dc.date.accessioned 2007-09-20T18:11:53Z
dc.date.available 2008-04-03T18:49:37Z en_US
dc.date.issued 2007-02-20 en_US
dc.identifier.citation Hsu, 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.issn 0277-6715 en_US
dc.identifier.issn 1097-0258 en_US
dc.identifier.uri http://hdl.handle.net/2027.42/55943
dc.identifier.uri http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=16755528&dopt=citation en_US
dc.description.abstract We 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.extent 119484 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 Multiple imputation for interval censored data with auxiliary variables 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, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, U.S.A. en_US
dc.contributor.affiliationum Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, U.S.A. en_US
dc.contributor.affiliationother 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. ; 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.affiliationother INSERM E0338 Biostatistics, ISPED, Bordeaux 2 University, Bordeaux 33000, France en_US
dc.identifier.pmid 16755528 en_US
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/55943/1/2581_ftp.pdf en_US
dc.identifier.doi http://dx.doi.org/10.1002/sim.2581 en_US
dc.identifier.source Statistics in Medicine en_US
dc.owningcollname Interdisciplinary and Peer-Reviewed
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