Disease mapping models: an empirical evaluation

The analysis of small area disease incidence has now developed to a degree where many methods have been proposed. However, there are few studies of th e relative merits of the methods available. While many Bayesian models have been examined with respect to prior sensitivity, it is clear that wider co mparisons of methods are largely missing from the literature. In this paper we present some preliminary results concerning the goodness-of-fit of a va riety of disease mapping methods to simulated data for disease incidence de rived from a range of models. These simulated models cover simple risk grad ients to more complex true risk structures, including spatial correlation. The main general results presented here show that the gamma-Poisson exchang eable model and the Besag, York and Mollie (BYM) model are most robust acro ss a range of diverse models. Mixture models are less robust. Non-parametri c smoothing methods perform badly in general. Linear Bayes methods display behaviour similar to that of the gamma-Poisson methods. Copyright (C) 2000 John Wiley & Sons, Ltd.