Ignoring overdispersion in hierarchical loglinear models: Possible problems and solutions
Elasma Milanzi, Ariel Alonso, Geert Molenberghs
STATISTICS IN MEDICINE | WILEY-BLACKWELL | Published : 2012
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
Awarded by IAP research network of the Belgian Government (Belgian Science Policy)
Financial support from the IAP research network #P6/03 of the Belgian Government (Belgian Science Policy) is gratefully acknowledged.