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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/13798

Title: Ignoring overdispersion in hierarchical loglinear models: Possible problems and solutions
Authors: Milanzi, Elasma
Alonso Abad, Ariel
Molenberghs, Geert
Issue Date: 2012
Citation: STATISTICS IN MEDICINE, 31 (14), p. 1475-1482
Abstract: Poisson data frequently exhibit overdispersion; and, for univariate models, many options exist to circumvent this problem. Nonetheless, in complex scenarios, for example, in longitudinal studies, accounting for overdispersion is a more challenging task. Recently, Molenberghs et.al, presented a model that accounts for overdispersion by combining two sets of random effects. However, introducing a new set of random effects implies additional distributional assumptions for intrinsically unobservable variables, which has not been considered before. Using the combined model as a framework, we explored the impact of ignoring overdispersion in complex longitudinal settings via simulations. Furthermore, we evaluated the effect of misspecifying the random-effects distribution on both the combined model and the classical Poisson hierarchical model. Our results indicate that even though inferences may be affected by ignored overdispersion, the combined model is a promising tool in this scenario. Copyright (C) 2012 John Wiley & Sons, Ltd.
Notes: [Milanzi, Elasma; Molenberghs, Geert] Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. [Alonso, Ariel] Maastricht Univ, Dept Methodol & Stat, Maastricht, Netherlands. [Molenberghs, Geert] Katholieke Univ Leuven, I BioStat, B-3000 Louvain, Belgium.
URI: http://hdl.handle.net/1942/13798
DOI: 10.1002/sim.4482
ISI #: 000304906800006
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2013
Appears in Collections: Research publications

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