Non-parametric estimation of gap time survival functions for ordered multivariate failure time data
dc.contributor.author | Schaubel, Douglas E. | en_US |
dc.contributor.author | Cai, Jianwen | en_US |
dc.date.accessioned | 2006-04-19T13:54:44Z | |
dc.date.available | 2006-04-19T13:54:44Z | |
dc.date.issued | 2004-06-30 | en_US |
dc.identifier.citation | Schaubel, Douglas E.; Cai, Jianwen (2004)."Non-parametric estimation of gap time survival functions for ordered multivariate failure time data." Statistics in Medicine 23(12): 1885-1900. <http://hdl.handle.net/2027.42/34861> | en_US |
dc.identifier.issn | 0277-6715 | en_US |
dc.identifier.issn | 1097-0258 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/34861 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15195322&dopt=citation | en_US |
dc.description.abstract | Times between sequentially ordered events (gap times) are often of interest in biomedical studies. For example, in a cancer study, the gap times from incidence-to-remission and remission-to-recurrence may be examined. Such data are usually subject to right censoring, and within-subject failure times are generally not independent. Statistical challenges in the analysis of the second and subsequent gap times include induced dependent censoring and non-identifiability of the marginal distributions. We propose a non-parametric method for constructing one-sample estimators of conditional gap-time specific survival functions. The estimators are uniformly consistent and, upon standardization, converge weakly to a zero-mean Gaussian process, with a covariance function which can be consistently estimated. Simulation studies reveal that the asymptotic approximations are appropriate for finite samples. Methods for confidence bands are provided. The proposed methods are illustrated on a renal failure data set, where the probabilities of transplant wait-listing and kidney transplantation are of interest. Copyright © 2004 John Wiley & Sons, Ltd. | en_US |
dc.format.extent | 166889 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | John Wiley & Sons, Ltd. | en_US |
dc.subject.other | Mathematics and Statistics | en_US |
dc.title | Non-parametric estimation of gap time survival functions for ordered multivariate failure time data | 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, Ann Arbor, MI 48109-2029, U.S.A. ; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, U.S.A. | en_US |
dc.contributor.affiliationother | Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, U.S.A. | en_US |
dc.identifier.pmid | 15195322 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/34861/1/1777_ftp.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1002/sim.1777 | en_US |
dc.identifier.source | Statistics in Medicine | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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