Ignoring overdispersion in hierarchical loglinear models: Possible problems and solutions

Elasma Milanzi*, Ariel Alonso, Geert Molenberghs

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Pages (from-to)1475-1482
JournalStatistics in Medicine
Issue number14
Publication statusPublished - 30 Jun 2012


  • Poisson-normal model
  • overdispersion
  • hierachical
  • combined model
  • Type I error

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