title: Bayesian estimates of disease maps: how important are priors?
abstract: In the fully Bayesian (FB) approach to disease mapping the choice of the hyperprior distribution of the dispersion parameter is a key issue. In this context we investigated the sensitivity of the rate ratio estimates to the choice of the hyperprior via a simulation study. We also compared the performance of the FB approach to mapping disease risk to the conventional approach of mapping maximum likelihood (ML) estimates and p-values. The study was modelled on the incidence data of insulin dependent diabetes mellitus (IDDM) as observed in the communes of Sardinia.
authors.affiliation: Istituto Scienze Sanitarie Applicate, Universitá degli Studi di Pavia, Italy.
authors.forename: L
authors.initials: L
authors.lastname: Bernardinelli
authors.forename: D
authors.initials: D
authors.lastname: Clayton
authors.forename: C
authors.initials: C
authors.lastname: Montomoli
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003922:Diabetes Mellitus, Type 1 / Q000453:epidemiology; D015994:Incidence; D007558:Italy / Q000453:epidemiology; D015233:Models, Statistical; D016012:Poisson Distribution; D012306:Risk