Ignoring overdispersion in hierarchical loglinear models: Possible problems and solutions

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.

Publication year:2012

Keywords:Poisson-normal model, overdispersion, combined model, Type I error, Mathematical & Computational Biology, Public, Environmental & Occupational Health, Medical Informatics, Medicine, Research & Experimental, Statistics & Probability, hierarchical, hierachical

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BOF-publication weight:1

CSS-citation score:1

Authors:International

Authors from:Higher Education

Accessibility:Open