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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17875

Title: Empirical Bayes estimates for correlated hierarchical data with overdispersion
Authors: Iddi, Samuel
Molenberghs, Geert
Aregay, Mehreteab
Kalema, George
Issue Date: 2014
Citation: PHARMACEUTICAL STATISTICS, 13 (5), p. 316-326
Abstract: An extension of the generalized linear mixed model was constructed to simultaneously accommodate overdispersion and hierarchies present in longitudinal or clustered data. This so-called combined model includes conjugate random effects at observation level for overdispersion and normal random effects at subject level to handle correlation, respectively. A variety of data types can be handled in this way, using different members of the exponential family. Both maximum likelihood and Bayesian estimation for covariate effects and variance components were proposed. The focus of this paper is the development of an estimation procedure for the two sets of random effects. These are necessary when making predictions for future responses or their associated probabilities. Such (empirical) Bayes estimates will also be helpful in model diagnosis, both when checking the fit of the model as well as when investigating outlying observations. The proposed procedure is applied to three datasets of different outcome types. Copyright (c) 2014 John Wiley & Sons, Ltd.
Notes: Molenberghs, G (reprint author), Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. geert.molenberghs@uhasselt.be
URI: http://hdl.handle.net/1942/17875
Link to publication: https://www.academia.edu/21369935/Empirical_Bayes_estimates_for_correlated_hierarchical_data_with_overdispersion
DOI: 10.1002/pst.1635
ISI #: 000342773200006
ISSN: 1539-1604
Category: A1
Type: Journal Contribution
Validation: ecoom, 2015
Appears in Collections: Research publications

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