Evaluation of cluster recovery for small area relative risk models

Abstract

The analysis of disease risk is often considered via relative risk. The comparison of relative risk estimation methods with "true risk" scenarios has been considered on various occasions. However, there has been little examination of how well competing methods perform when the focus is clustering of risk. In this paper, a simulated evaluation of a range of potential spatial risk models and a range of measures that can be used for (a) cluster goodness of fit, (b) cluster diagnostics are considered. Results suggest that exceedence probability is a poor measure of hot spot clustering because of model dependence, whereas residual-based methods are less model dependent and perform better. Local deviance information criteria measures perform well, but conditional predictive ordinate measures yield a high false positive rate.