Latency and time-dependent exposure in a case-control study
dc.contributor.author | Moulton, Lawrence H. | en_US |
dc.contributor.author | Le, Monique G. | en_US |
dc.date.accessioned | 2006-04-10T14:56:09Z | |
dc.date.available | 2006-04-10T14:56:09Z | |
dc.date.issued | 1991 | en_US |
dc.identifier.citation | Moulton, Lawrence H., G. Le, Monique (1991)."Latency and time-dependent exposure in a case-control study." Journal of Clinical Epidemiology 44(9): 915-923. <http://hdl.handle.net/2027.42/29651> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6T84-4CPMD5X-DP/2/70cf49bdc908f300dffd8bfb7c858cd8 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/29651 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=1890434&dopt=citation | en_US |
dc.description.abstract | Detailed historical data are elicited often from subjects in retrospective studies, yielding time-dependent measures of exposures. Investigation of a hypothesized period of latency can be made by examining disease/exposure relationships in multiple time windows, either along the age or time-before diagnosis axes. We suggest splitting the data into many time intervals and separately fitting regression models tothe available data in each interval. Covariances between estimated coefficients from different intervals are empirically estimated, and used for assessing variability of specified functions of the time-specific coefficients. Alternative methods of interval formation and their consequences are discussed. We apply these methods to a French case-control study of oral contraceptive use and cervical cancer incidence, and compare the results to those of standard analyses. | en_US |
dc.format.extent | 956438 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | Latency and time-dependent exposure in a case-control study | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Public Health | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationother | I.N.S.E.R.M. Unit 287, l'Institut Gustave-Roussy, 94805, Villejuif Cedex, France | en_US |
dc.identifier.pmid | 1890434 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/29651/1/0000740.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0895-4356(91)90054-D | en_US |
dc.identifier.source | Journal of Clinical Epidemiology | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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