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Summary

This study demonstrates fitting complex spatio-temporal disease mapping models using NIMBLE, a flexible software package. It highlights using sum-to-zero constraints in NIMBLE for accurate analysis of disease risk over space and time.

Keywords:
Disease mappingINLANIMBLEidentifiabilityspatio-temporal interactionssum-to-zero constraints

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Spatial Analysis

Background:

  • Spatio-temporal disease mapping analyzes disease risk distribution and temporal changes.
  • Hierarchical Poisson mixed models are commonly used but complex for practitioners.
  • Model identifiability issues arise, often requiring constraints that are not easily implemented in all software.

Purpose of the Study:

  • To demonstrate fitting spatio-temporal disease mapping models using the NIMBLE software.
  • To emphasize the implementation of sum-to-zero constraints in NIMBLE for handling identifiability issues, particularly with spatio-temporal interactions.
  • To compare NIMBLE with R-INLA for parameter and relative risk estimation.

Main Methods:

  • Utilized NIMBLE, a package offering configurable Markov chain Monte Carlo (MCMC) algorithms.
  • Implemented sum-to-zero constraints within NIMBLE to address identifiability problems in spatio-temporal models.
  • Applied models to breast cancer mortality data in Spain (1990-2010).
  • Conducted a simulation study for comparative analysis.

Main Results:

  • NIMBLE successfully fitted spatio-temporal disease mapping models with sum-to-zero constraints.
  • Parameter estimates and relative risk estimations from NIMBLE were comparable to R-INLA.
  • Differences were noted in computational time, with R-INLA potentially being faster in some scenarios.

Conclusions:

  • NIMBLE provides a viable and flexible platform for fitting complex spatio-temporal disease mapping models.
  • The use of sum-to-zero constraints in NIMBLE effectively resolves identifiability issues in spatio-temporal analyses.
  • While results are similar, computational efficiency should be considered when choosing between NIMBLE and R-INLA.