Bayesian joint spatio-temporal analysis of multiple diseases

  1. Virgilio Gómez-Rubio
  2. Francisco Palmí-Perales
  3. Gonzalo López-Abente
  4. Rebeca Ramis-Prieto
  5. Pablo Fernández-Navarro
Journal:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Year of publication: 2019

Volume: 43

Issue: 1

Pages: 51-74

Type: Article

DOI: 10.2436/20.8080.02.79 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Sort: Statistics and Operations Research Transactions

Abstract

In this paper we propose a Bayesian hierarchical spatio-temporal model for the joint analysis of multiple diseases which includes specific and shared spatial and temporal effects. Dependence on shared terms is controlled by disease-specific weights so that their posterior distribution can be used to identify diseases with similar spatial and temporal patterns. The model proposed here has been used to study three different causes of death (oral cavity, esophagus and stomach cancer) in Spain at the province level. Shared and specific spatial and temporal effects have been estimated and mapped in order to study similarities and differences among these causes. Furthermore, estimates using Markov chain Monte Carlo and the integrated nested Laplace approximation are compared.

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Funding information

This work has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/ 000491, funded by Consejería de Educación, Cultura y Deportes (Castilla-La Man-cha, Spain) and Fondo Europeo de Desarrollo Regional, and grant MTM2016-77501-P, funded by the Ministerio de Economía y Competitividad (Spain). F. Palmí-Perales was supported by a doctoral scholarship awarded by the University of Castilla-La Mancha (Spain). We also thank Prof. Håvard Rue for his help with the implementation of the model using INLA.

Funders

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