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Modelling risk from a disease in time and space
Author(s) -
KnorrHeld Leonhard,
Besag Julian
Publication year - 1998
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19980930)17:18<2045::aid-sim943>3.0.co;2-p
Subject(s) - computer science , disease , space (punctuation) , econometrics , statistics , medicine , mathematics , operating system
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. © 1998 John Wiley & Sons, Ltd.