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On the convenience of heteroscedasticity in highly multivariate disease mapping

  • F. Corpas-Burgos
  • P. Botella-Rocamora
  • M. A. Martinez-BeneitoEmail author
Original Paper

Abstract

Highly multivariate disease mapping has recently been proposed as an enhancement of traditional multivariate studies, making it possible to perform the joint analysis of a large number of diseases. This line of research has an important potential since it integrates the information of many diseases into a single model yielding richer and more accurate risk maps. In this paper we show how some of the proposals already put forward in this area display some particular problems when applied to small regions of study. Specifically, the homoscedasticity of these proposals may produce evident misfits and distorted risk maps. In this paper we propose two new models to deal with the variance-adaptivity problem in multivariate disease mapping studies and give some theoretical insights on their interpretation.

Keywords

Gaussian Markov random fields Multivariate disease mapping Bayesian statistics Spatial statistics Mortality studies 

Mathematics Subject Classification

62P10-Applications to biology and medical sciences 

Notes

Acknowledgements

The authors acknowledge the support of the research Grant PI16/01004 (co-funded with FEDER grants) of Instituto de Salud Carlos III and predoctoral contract UGP-15-156 of FISABIO.

Supplementary material

11749_2019_628_MOESM1_ESM.pdf (2.9 mb)
Supplementary material 1 (pdf 2954 KB)
11749_2019_628_MOESM2_ESM.pdf (174 kb)
Supplementary material 2 (pdf 174 KB)
11749_2019_628_MOESM3_ESM.pdf (156 kb)
Supplementary material 3 (pdf 156 KB)

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Copyright information

© Sociedad de Estadística e Investigación Operativa 2019

Authors and Affiliations

  1. 1.Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana (FISABIO)ValenciaSpain
  2. 2.Subdirección de Epidemiología, Vigilancia de la Salud y Sanidad Ambiental, Conselleria de Sanitat Universal i Salut PúblicaValenciaSpain
  3. 3.CIBER de Epidemiología y Salud PúblicaMadridSpain

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