Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2019

Bayesian joint spatio-temporal analysis of multiple diseases

  • Dades identificatives

    Identificador: RP:3408
    Handle: https://hdl.handle.net/20.500.11797/RP3408
    Autors:
    Fernández-Navarro, PabloRamis-Prieto, RebecaLópez-Abente, GonzaloPalmí-Perales, FranciscoGómez-Rubio, Virgilio
    Resum:
    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.

  • Altres:

    Autor/s de la URV: Fernández-Navarro, Pablo Ramis-Prieto, Rebeca López-Abente, Gonzalo Palmí-Perales, Francisco Gómez-Rubio, Virgilio
    Paraules clau: Bayesian modelling, Joint modelling, Multivariate disease mapping, Shared components. Spatio-temporal epidemiology
    Resum: 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.
    Any de publicació de la revista: 2019
    Tipus de publicació: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Paraules clau:

    Bayesian modelling, Joint modelling, Multivariate disease mapping, Shared components. Spatio-temporal epidemiology

  • Documents:

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