title: Ignoring overdispersion in hierarchical loglinear models: Possible problems and solutions.
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.
authors.affiliation: I-BioStat, Universiteit Hasselt, B-3590 Diepenbeek, Belgium. elasma.milanzi@uhasselt.be
authors.forename: Elasma
authors.initials: E
authors.lastname: Milanzi
authors.forename: Ariel
authors.initials: A
authors.lastname: Alonso
authors.forename: Geert
authors.initials: G
authors.lastname: Molenberghs
mesh_terms: D000927:Anticonvulsants / Q000627:therapeutic use; D003198:Computer Simulation / Q000706:statistics & numerical data; D004827:Epilepsy / Q000188:drug therapy; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies / Q000706:statistics & numerical data; D008954:Models, Biological; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data; D016012:Poisson Distribution; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data
chemical_list: D000927:Anticonvulsants