A spatially discrete approximation to log‐Gaussian Cox processes for modelling aggregated disease count data. (26th August 2019)

• Record Type: Journal Article
• Authors: Johnson, Olatunji; Diggle, Peter; Giorgi, Emanuele
• Is Part Of: Statistics in medicine. Volume 38:Number 24(2019)
• Page Start: 4871
• Page End: 4887
• Publication Date: 2019-08-26
• Subjects: disease mapping -- geostatistics -- log‐Gaussian Cox process -- Monte Carlo maximum likelihood; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8339
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : In this paper, we develop a computationally efficient discrete approximation to log‐Gaussian Cox process (LGCP) models for the analysis of spatially aggregated disease count data. Our approach overcomes an inherent limitation of spatial models based on Markov structures, namely, that each such model is tied to a specific partition of the study area, and allows for spatially continuous prediction. We compare the predictive performance of our modelling approach with LGCP through a simulation study and an application to primary biliary cirrhosis incidence data in Newcastle upon Tyne, UK. Our results suggest that, when disease risk is assumed to be a spatially continuous process, the proposed approximation to LGCP provides reliable estimates of disease risk both on spatially continuous and aggregated scales. The proposed methodology is implemented in the open‐source R packageSDALGCP .