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On fitting spatio-temporal disease mapping models using approximate Bayesian inference.

María Dolores Ugarte1, Aritz Adin2, Tomas Goicoa3

  • 1Department of Statistics and O. R., Public University of Navarre, Spain lola@unavarra.es.

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Summary
This summary is machine-generated.

This study introduces integrated nested Laplace approximations (INLA) for complex spatio-temporal disease mapping, offering a faster alternative to Markov chain Monte Carlo (MCMC) methods. INLA efficiently analyzes disease distribution and evolution, demonstrated with male brain cancer data in Spain.

Keywords:
Brain cancerINLALeroux CAR priorPQLspace–time interactions

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Geographic Information Systems (GIS)

Background:

  • Spatio-temporal disease mapping models disease distribution and evolution over space and time.
  • Commonly use hierarchical Bayesian frameworks with Empirical Bayes (EB) and Fully Bayes (FB) approaches.
  • Traditional FB methods often rely on Markov Chain Monte Carlo (MCMC), which can be computationally intensive and prone to errors with large datasets.

Purpose of the Study:

  • To introduce and evaluate Integrated Nested Laplace Approximations (INLA) for Bayesian inference in complex spatio-temporal disease mapping models.
  • To demonstrate the application of INLA using the Leroux CAR prior for spatial components.
  • To compare the performance of INLA against the Penalized Quasi-Likelihood (PQL) method via simulation and real-world data analysis.

Main Methods:

  • Implementation of spatio-temporal disease mapping models using INLA.
  • Utilizing the Leroux Conditional Autoregressive (CAR) prior for spatial effects.
  • Comparison with the Penalized Quasi-Likelihood (PQL) estimation technique through simulation studies.
  • Analysis of male brain cancer mortality in Spain (1986-2010) using the proposed INLA approach.

Main Results:

  • INLA provides an efficient alternative to MCMC for complex spatio-temporal models, reducing computation time and potential Monte Carlo errors.
  • The study demonstrates the feasibility of fitting these models with INLA and the Leroux CAR prior.
  • The analysis of Spanish male brain cancer mortality showcases the practical application and insights gained from INLA.

Conclusions:

  • INLA is a powerful and efficient tool for Bayesian inference in spatio-temporal disease mapping, especially for complex latent Gaussian models.
  • It offers a viable and computationally advantageous alternative to traditional MCMC methods.
  • The method facilitates robust analysis of disease patterns and trends, as evidenced by the brain cancer mortality study.