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Comparison of different software implementations for spatial disease mapping
Spatial and Spatio-Temporal Epidemiology
|November 4, 2019
Summary
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.
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.
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