Local Likelihood Disease Clustering: Development and Evaluation

• Activity Number: 106
• Type: Contributed
• Date/Time: Monday, August 8, 2005 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Monir Hossain*+ and Andrew Lawson
• Companies: University of South Carolina and University of South Carolina
• Keywords: disease clustering ; local likelihood ; covariate dependence ; CAR prior ; absolute difference prior ; edge effect

Abstract

This paper illustrates a method based on local likelihood for detecting disease clusters. The approach is based on estimating a lasso distance for each region considered to be clustered. An important advantage of implementing this approach is that it does not require any special MCMC algorithm, (e.g., reversible jump MCMC), which is essential in hidden Markov model approach. Another advantage is that extending the model to incorporate covariates is straightforward. We illustrate three ways of doing this by using Eastern Germany lip cancer data. By using simulated data, we made a comparison with the BYM model (Besag et al. 1991) and the mixture model (Lawson and Clark 2002). We also did a limited examination of the ability of the local likelihood model to recover true relative risk under different priors for lasso parameter. In order to check the edge effects---which has been overlooked in many spatial clustering models for disease mapping, but deserves special attention as it lacks observable neighbors---we have adapted here a simple approach to observe the changes in relative risks when the edge regions are omitted.