Modelling 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 implemented via Markov chain Monte Carlo methods. The methodology is illustrated by a tentative re-analysis of Ohio lung cancer data 1968-1988. Two approaches that adjust for unmeasured spatial covariates, particularly tobacco consumption, are described. The first includes random effects in the model to account for unobserved heterogeneity; the second adds a simple urbanization measure as a surrogate for smoking behaviour. The Ohio data set has been of particular interest because of the suggestion that a nuclear facility in the southwest of the state may have caused increased levels of lung cancer there. However, we contend here that the data are inadequate for a proper investigation of this issue.
Knorr-Held
L
L
Institut für Statistik, Universität München, Germany.
Besag
J
J
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D001499:Bayes Theorem; D002648:Child; D005006:Ethnicity; D006801:Humans; D008175:Lung Neoplasms / Q000401:mortality*; D008390:Markov Chains; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D009820:Ohio; D012306:Risk; D012307:Risk Factors; D012737:Sex Factors; D012907:Smoking / Q000009:adverse effects; D013269:Stochastic Processes; D014507:Urbanization; D044465:White People