Multiple imputation for interval censored data with auxiliary variables
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 | https://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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