Quadratic inference functions in marginal models for longitudinal data
dc.contributor.author | Song, Peter X.-K. | en_US |
dc.contributor.author | Jiang, Zhichang | en_US |
dc.contributor.author | Park, Eunjoo | en_US |
dc.contributor.author | Qu, Annie | en_US |
dc.date.accessioned | 2010-01-05T15:11:11Z | |
dc.date.available | 2010-03-01T21:10:29Z | en_US |
dc.date.issued | 2009-12-20 | en_US |
dc.identifier.citation | Song, Peter X.-K.; Jiang, Zhichang; Park, Eunjoo; Qu, Annie (2009). "Quadratic inference functions in marginal models for longitudinal data." Statistics in Medicine 28(29): 3683-3696. <http://hdl.handle.net/2027.42/64550> | en_US |
dc.identifier.issn | 0277-6715 | en_US |
dc.identifier.issn | 1097-0258 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/64550 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19757486&dopt=citation | en_US |
dc.description.abstract | The quadratic inference function (QIF) is a new statistical methodology developed for the estimation and inference in longitudinal data analysis using marginal models. This method is an alternative to the popular generalized estimating equations approach, and it has several useful properties such as robustness, a goodness-of-fit test and model selection. This paper presents an introductory review of the QIF, with a strong emphasis on its applications. In particular, a recently developed SAS MACRO QIF is illustrated in this paper to obtain numerical results. Copyright © 2009 John Wiley & Sons, Ltd. | en_US |
dc.format.extent | 148559 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 | Quadratic inference functions in marginal models for longitudinal 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, UM School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029, U.S.A. ; Department of Biostatistics, UM School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029, U.S.A. | en_US |
dc.contributor.affiliationother | Alberta Cancer Board, Edmonton, AB, Canada T6G 1Z2 | en_US |
dc.contributor.affiliationother | St. Paul's Hospital, Vancouver, BC, Canada V6Z 1T6 | en_US |
dc.contributor.affiliationother | Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, U.S.A. | en_US |
dc.identifier.pmid | 19757486 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/64550/1/3719_ftp.pdf | |
dc.identifier.doi | 10.1002/sim.3719 | en_US |
dc.identifier.source | Statistics in Medicine | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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