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Statistical Methods in Medical Research
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Bayesian spatio-temporal analysis of joint patterns of male and female lung cancer risks in Yorkshire (UK)

Sylvia Richardson

Small Area Health Statistics Unit (SAHSU), Department of Epidemiology and Public Health, Imperial College London, UK, sylvia.richardson{at}imperial.ac.uk

Juan J Abellan

Small Area Health Statistics Unit (SAHSU), Department of Epidemiology and Public Health, Imperial College London, UK

Nicky Best

Small Area Health Statistics Unit (SAHSU), Department of Epidemiology and Public Health, Imperial College London, UK

Recent advances in disease mapping have focused first on including the time dimension, thus giving rise to spatio-temporal analysis of the variation of disease risk and, secondly, on carrying out joint analysis of two diseases that share common environmental risk factors and are, therefore, related. Here, we try to combine both issues and present a joint analysis of the spatio-temporal variation of the risks of two related diseases processes-male and female lung cancer incidence-in a region of England. To do so, we use a Bayesian hierarchical model that splits the risk of disease into two spatio-temporal components: a shared component and a specific component that calibrates the differential between the two diseases.

Statistical Methods in Medical Research, Vol. 15, No. 4, 385-407 (2006)
DOI: 10.1191/0962280206sm458oa
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This article has been cited by other articles:


Home page
 Stat Methods Med ResHome page
E. Tzala and N. Best
Bayesian latent variable modelling of multivariate spatio-temporal variation in cancer mortality
Statistical Methods in Medical Research, February 1, 2008; 17(1): 97 - 118.
[Abstract] [PDF]


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