A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data

Johnson, Olatunji and Diggle, Peter and Giorgi, Emanuele (2019) A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data. Statistics in Medicine, 38 (24). pp. 4871-4887. ISSN 0277-6715

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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 package SDALGCP.

Item Type:
Journal Article
Journal or Publication Title:
Statistics in Medicine
Additional Information:
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.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
136345
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Deposited On:
27 Aug 2019 08:20
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Sep 2020 05:26