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Comparison of different software implementations for spatial disease mapping.

M Vranckx1, T Neyens1, C Faes1

  • 1I-BioStat, Hasselt University, Diepenbeek, Belgium.

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|November 4, 2019
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Summary
This summary is machine-generated.

This study compares disease mapping software for predicting disease risk using spatial data. It evaluates R-packages CARBayes, R2OpenBUGS, NIMBLE, R2BayesX, R-INLA, and RStan for conditional autoregressive (CAR) models.

Keywords:
Conditional autoregressive modelsDiabeticsDisease mappingRelative risksSoftware packages

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Area of Science:

  • Spatial epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Disease mapping analyzes disease risk using spatial counts.
  • Hierarchical models with conditional autoregressive (CAR) effects are popular for disease mapping.
  • Various software packages exist, but methodological differences can impact results.

Purpose of the Study:

  • To compare analysis results from different R-packages for CAR models in disease mapping.
  • To assess the performance of CARBayes, R2OpenBUGS, NIMBLE, R2BayesX, R-INLA, and RStan.
  • To evaluate software suitability for spatially discrete count data analysis.

Main Methods:

  • Investigated conditional autoregressive (CAR) models for disease mapping.
  • Utilized a case study of childhood diabetes in Belgium.
  • Conducted simulation studies to assess software performance under various conditions.

Main Results:

  • Comparative analysis of disease mapping software outputs.
  • Evaluation of model performance across different R-packages.
  • Identification of software strengths and weaknesses for spatial count data.

Conclusions:

  • Software choice impacts disease mapping analysis results.
  • Understanding methodological differences is crucial for accurate risk prediction.
  • The study provides guidance for selecting appropriate software for spatial epidemiological studies.