Fixed versus Mixed Parameterization in Logistic Regression Models: Application to Meta-Analysis

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dc.contributor.advisorSlud, Eric V.en_US
dc.contributor.authorWeng, Chin-Fangen_US
dc.contributor.departmentMathematical Statisticsen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2009-03-24T05:36:54Z
dc.date.available2009-03-24T05:36:54Z
dc.date.issued2008en_US
dc.description.abstractThree methods: fixed intercept generalized model (GLM), random intercept generalized mixed model (GLMM), and conditional logistic regression (clogit) are compared in a meta-analysis of 43 studies assessing the effect of diet on cancer incidence in rats. We also perform simulation studies to assess distributional behavior of regression estimates and tests of fit. Other simulations assess the effects of model misspecification, and increasing the sample size, either by adding additional studies or by increasing the sizes of a fixed number of studies. Estimates of fixed effects seem insensitive to increasing the sample sizes, but the deviance test of fit is biased. Conditional logistic regression avoids the possibility of bias when the number of studies is very large in a GLM analysis and also avoids effects of misspecification of the random effect distribution in a GLMM analysis, but at the cost of some information loss.en_US
dc.format.extent720132 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/8985
dc.language.isoen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledconditional logistic regressionen_US
dc.subject.pquncontrolledfixed effectsen_US
dc.subject.pquncontrolledmany nuisance parametersen_US
dc.subject.pquncontrolledmixed effectsen_US
dc.subject.pquncontrolledsimulationen_US
dc.titleFixed versus Mixed Parameterization in Logistic Regression Models: Application to Meta-Analysisen_US
dc.typeThesisen_US

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