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Towards joint disease mapping

Department of Statistics, University of Munich, Germany, held{at}stat.uni-muenchen.de

Isabel Natário

CEAUL, Faculdade de Ciências da Universidade de Lisboa and Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Portugal

Sarah Elaine Fenton

Department of Veterinary Clinical Sciences and Department of Mathematical Sciences, University of Liverpool, UK

Håvard Rue

Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway

Nikolaus Becker

German Cancer Research Center, Heidelberg, Germany

This article discusses and extends statistical models to jointly analyse the spatial variation of rates of several diseases with common risk factors. We start with a review of methods for separate analyses of diseases, then move to ecological regression approaches, where the rates from one of the diseases enter as surrogate covariates for exposure. Finally, we propose a general framework for jointly modelling the variation of two or more diseases, some of which share latent spatial fields, but with possibly different risk gradients. In our application, we consider mortality data on oral, oesophagus, larynx and lung cancers for males in Germany, which all share smoking as a common risk factor. Furthermore, the first three cancers are also known to be related to excessive alcohol consumption. An empirical comparison of the different models based on a formal model criterion as well as on the posterior precision of the relative risk estimates strongly suggests that the joint modelling approach is a useful and valuable extension over individual analyses.

Statistical Methods in Medical Research, Vol. 14, No. 1, 61-82 (2005)
DOI: 10.1191/0962280205sm389oa


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