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. 2017 Mar;12(1):239-259.
doi: 10.1214/16-BA995. Epub 2016 Mar 18.

Towards a Multidimensional Approach to Bayesian Disease Mapping

Miguel A Martinez-Beneito  1 , Paloma Botella-Rocamora  2 , Sudipto Banerjee  3
Affiliations

Towards a Multidimensional Approach to Bayesian Disease Mapping

Miguel A Martinez-Beneito et al. Bayesian Anal. 2017 Mar.

Abstract

Multivariate disease mapping enriches traditional disease mapping studies by analysing several diseases jointly. This yields improved estimates of the geographical distribution of risk from the diseases by enabling borrowing of information across diseases. Beyond multivariate smoothing for several diseases, several other variables, such as sex, age group, race, time period, and so on, could also be jointly considered to derive multivariate estimates. The resulting multivariate structures should induce an appropriate covariance model for the data. In this paper, we introduce a formal framework for the analysis of multivariate data arising from the combination of more than two variables (geographical units and at least two more variables), what we have called Multidimensional Disease Mapping. We develop a theoretical framework containing both separable and non-separable dependence structures and illustrate its performance on the study of real mortality data in Comunitat Valenciana (Spain).

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Figures

Figure 1
Figure 1
Posterior mean of the Relative Risk for every municipality. Results in the first row correspond to the Colon/Rectum study and those in the second row correspond to the Lung/Diabetes study.
See this image and copyright information in PMC

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