Journal article

Ignoring overdispersion in hierarchical loglinear models: Possible problems and solutions

Elasma Milanzi, Ariel Alonso, Geert Molenberghs

STATISTICS IN MEDICINE | WILEY-BLACKWELL | Published : 2012

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. Furthermo..

View full abstract