Bayesian Analysis

Towards a Multidimensional Approach to Bayesian Disease Mapping

Miguel A. Martinez-Beneito, Paloma Botella-Rocamora, and Sudipto Banerjee

Full-text: Open access

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).

Article information

Source
Bayesian Anal., Volume 12, Number 1 (2017), 239-259.

Dates
First available in Project Euclid: 18 March 2016

Permanent link to this document
https://projecteuclid.org/euclid.ba/1458324098

Digital Object Identifier
doi:10.1214/16-BA995

Mathematical Reviews number (MathSciNet)
MR3597574

Zentralblatt MATH identifier
1384.62308

Citation

Martinez-Beneito, Miguel A.; Botella-Rocamora, Paloma; Banerjee, Sudipto. Towards a Multidimensional Approach to Bayesian Disease Mapping. Bayesian Anal. 12 (2017), no. 1, 239--259. doi:10.1214/16-BA995. https://projecteuclid.org/euclid.ba/1458324098


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