Show simple item record

Quadratic inference functions in marginal models for longitudinal data

dc.contributor.authorSong, Peter X.-K.en_US
dc.contributor.authorJiang, Zhichangen_US
dc.contributor.authorPark, Eunjooen_US
dc.contributor.authorQu, Annieen_US
dc.date.accessioned2010-01-05T15:11:11Z
dc.date.available2010-03-01T21:10:29Zen_US
dc.date.issued2009-12-20en_US
dc.identifier.citationSong, 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.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/64550
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19757486&dopt=citationen_US
dc.description.abstractThe 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.extent148559 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherJohn Wiley & Sons, Ltd.en_US
dc.subject.otherMathematics and Statisticsen_US
dc.titleQuadratic inference functions in marginal models for longitudinal dataen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment 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.affiliationotherAlberta Cancer Board, Edmonton, AB, Canada T6G 1Z2en_US
dc.contributor.affiliationotherSt. Paul's Hospital, Vancouver, BC, Canada V6Z 1T6en_US
dc.contributor.affiliationotherDepartment of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, U.S.A.en_US
dc.identifier.pmid19757486en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64550/1/3719_ftp.pdf
dc.identifier.doi10.1002/sim.3719en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.