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oai:drum.lib.umd.edu:1903/89852016-03-29T06:30:11Zcom_1903_2261com_1903_12com_1903_2col_1903_2793col_1903_3

Slud, Eric V. Weng, Chin-Fang Digital Repository at the University of Maryland University of Maryland (College Park, Md.) Mathematical Statistics 2009-03-24T05:36:54Z 2009-03-24T05:36:54Z 2008 http://hdl.handle.net/1903/8985 Three 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. 720132 bytes application/pdf en_US Fixed versus Mixed Parameterization in Logistic Regression Models: Application to Meta-Analysis Thesis Statistics conditional logistic regression fixed effects many nuisance parameters mixed effects simulation open.access