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Missing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassification
dc.contributor.authorAhn, Jaeilen_US
dc.contributor.authorMukherjee, Bhramaren_US
dc.contributor.authorGruber, Stephen B.en_US
dc.contributor.authorSinha, Samiranen_US
dc.date.accessioned2011-11-10T15:35:57Z
dc.date.available2012-07-12T17:42:24Zen_US
dc.date.issued2011-06en_US
dc.identifier.citationAhn, Jaeil; Mukherjee, Bhramar; Gruber, Stephen B.; Sinha, Samiran (2011). "Missing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassification." Biometrics 67(2). <http://hdl.handle.net/2027.42/87006>en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.issn1541-0420en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/87006
dc.publisherBlackwell Publishing Incen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherConditional Likelihooden_US
dc.subject.otherNonignorable Missingnessen_US
dc.subject.otherProportional Oddsen_US
dc.subject.otherStages of Canceren_US
dc.subject.otherVector Generalized Linear Modelen_US
dc.titleMissing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassificationen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.en_US
dc.contributor.affiliationumDepartment of Epidemiology, Human Genetics and Internal Medicine, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.en_US
dc.contributor.affiliationotherDepartment of Statistics, Texas A&M University, College Station, Texas 77843, U.S.A.en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/87006/1/j.1541-0420.2010.01453.x.pdf
dc.identifier.doi10.1111/j.1541-0420.2010.01453.xen_US
dc.identifier.sourceBiometricsen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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