Disease Mapping and Regression with Count Data in the Presence of Overdispersion and Spatial Autocorrelation

• Activity Number: 353
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Mohammad Reza Mohebbi*+
• Companies: Deakin University
• Keywords: Bayesian variable selection ; cancer; ; disease mapping ; ecologic studies ; Gibbs sampling ; spatial epidemiology

Abstract

This poster describes the generalised linear model approach for modelling geographical variation to cancer incidence data. Cancer registry data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. We proposed regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and different autocorrelation structures. The framework of Bayesian variable selection and a Gibbs sampling based technique has been employed to identify significant cancer risk factors on a cancer registry data from Caspian region of Iran. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalis