Rights statement: This is the peer reviewed version of the following article: Johnson, O, Diggle, P, Giorgi, E. A spatially discrete approximation to log‐Gaussian Cox processes for modelling aggregated disease count data. Statistics in Medicine. 2019; 1– 17. https://doi.org/10.1002/sim.8339 which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/sim.8339 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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TY - JOUR
T1 - A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data
AU - Johnson, Olatunji
AU - Diggle, Peter
AU - Giorgi, Emanuele
N1 - This is the peer reviewed version of the following article: Johnson, O, Diggle, P, Giorgi, E. A spatially discrete approximation to log‐Gaussian Cox processes for modelling aggregated disease count data. Statistics in Medicine. 2019; 1– 17. https://doi.org/10.1002/sim.8339 which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/sim.8339 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2019/10/30
Y1 - 2019/10/30
N2 - 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 package SDALGCP.
AB - 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 package SDALGCP.
KW - disease mapping
KW - geostatistics
KW - log‐Gaussian Cox process
KW - Monte Carlo maximum likelihood
U2 - 10.1002/sim.8339
DO - 10.1002/sim.8339
M3 - Journal article
VL - 38
SP - 4871
EP - 4887
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 24
ER -