Marginalized Multilevel Models and Likelihood Inference

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dc.contributor.author Zeger, Scott L.
dc.contributor.author Heagerty, Patrick J.
dc.date.accessioned 2008-12-11T21:49:54Z
dc.date.available 2008-12-11T21:49:54Z
dc.date.issued 2000
dc.identifier.citation Heagerty, Patrick J. and Scott L. Zeger. "Marginalized Multilevel Models and Likelihood Inference" Statistical Science 15 (1)(2000):1-26 http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.ss/1009212671 en
dc.identifier.uri http://jhir.library.jhu.edu/handle/1774.2/32863
dc.description.abstract Hierarchical or "multilevel" regression models typically parameterize the mean response conditional on unobserved latent variables or "random" effects and then make simple assumptions regarding their distribution. The interpretation of a regression parameter in such a model is the change in possibly transformed mean response per unit change in a particular predictor having controlled for all conditioning variables including the random effects. An often overlooked limitation of the conditional formulation for nonlinear models is that the interpretation of regression coefficients and their estimates can be highly sensitive to difficult-to-verify assumptions about the distribution of random effects, particularly the dependence of the latent variable distribution on covariates. In this article, we present an alternative parameterization for the multilevel model in which the marginal mean, rather than the conditional mean given random effects, is regressed on covariates. The impact of random effects model violations on the marginal and more traditional conditional parameters is compared through calculation of asymptotic relative biases. A simple two-level example from a study of teratogenicity is presented where the binomial overdispersion depends on the binary treatment assignment and greatly influences likelihood-based estimates of the treatment effect in the conditional model. A second example considers a three-level structure where attitudes toward abortion over time are correlated with person and district level covariates. We observe that regression parameters in conditionally specified models are more sensitive to random effects assumptions than their counterparts in the marginal formulation. en
dc.description.provenance Submitted by David Reynolds (davidr@jhu.edu) on 2008-12-11T21:49:54Z No. of bitstreams: 1 2000-Marginalized.pdf: 249835 bytes, checksum: 0f1dbc5a9c45d9379fdf80896ff6c4c2 (MD5) en
dc.description.provenance Made available in DSpace on 2008-12-11T21:49:54Z (GMT). No. of bitstreams: 1 2000-Marginalized.pdf: 249835 bytes, checksum: 0f1dbc5a9c45d9379fdf80896ff6c4c2 (MD5) Previous issue date: 2000 en
dc.language.iso en_US en
dc.publisher Institute of Mathematical Statistics en
dc.subject Random effects model en
dc.subject Logistic regression en
dc.subject Latent variable en
dc.subject Generalized linear model en
dc.title Marginalized Multilevel Models and Likelihood Inference en
dc.type Article en

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