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  • 1901.09551

    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.

    Accepted author manuscript, 4.99 MB, PDF document

    Embargo ends: 26/08/20

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>30/10/2019
<mark>Journal</mark>Statistics in Medicine
Issue number24
Volume38
Number of pages17
Pages (from-to)4871-4887
Publication statusPublished
Early online date26/08/19
Original languageEnglish

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.

Bibliographic note

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.