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

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A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data. / Johnson, Olatunji; Diggle, Peter; Giorgi, Emanuele.

In: Statistics in Medicine, Vol. 38, No. 24, 30.10.2019, p. 4871-4887.

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@article{414a62f0db9e4c8bbded33b1fd8604d0,
title = "A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data",
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.",
keywords = "disease mapping, geostatistics, log‐Gaussian Cox process, Monte Carlo maximum likelihood",
author = "Olatunji Johnson and Peter Diggle and Emanuele Giorgi",
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. ",
year = "2019",
month = oct,
day = "30",
doi = "10.1002/sim.8339",
language = "English",
volume = "38",
pages = "4871--4887",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "24",

}

RIS

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 -