MODELING RISK FROM A DISEASE IN TIME AND SPACE

This paper combines existing models for longitudinal and spatial data in a hierarchical Bayesian framework, with particular emphasis on the role of time- and space-varying covariate effects. Data analysis is im plemented via Markov chain Monte Carlo methods. The methodology is ill ustrated by a tentative re-analysis of Ohio lung cancer data 1968-1988 . Two approaches that adjust for unmeasured spatial covariates, partic ularly tobacco consumption, are described. The first includes random e ffects in the model to account for unobserved heterogeneity; the secon d adds a simple urbanization measure as a surrogate for smoking behavi our. The Ohio data set has been of particular interest because of the suggestion that a nuclear facility in the southwest of the state may h ave caused increased levels of lung cancer there. However, we contend here that the data are inadequate for a proper investigation of this i ssue. (C) 1998 John Wiley & Sons, Ltd.