CHICAS, Lancaster Medical School, Lancaster University, Lancaster, UK.
Stat Med. 2019 Oct 30;38(24):4871-4887. doi: 10.1002/sim.8339. Epub 2019 Aug 26.
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.
在本文中，我们为分析空间聚集疾病计数数据开发了一种计算效率高的对数高斯 Cox 过程 (LGCP) 模型的离散逼近方法。我们的方法克服了基于马尔可夫结构的空间模型的一个固有局限性，即每个这样的模型都与研究区域的特定分区相关联，并且允许进行空间连续预测。我们通过模拟研究和对英国泰恩河畔纽卡斯尔原发性胆汁性肝硬化发病率数据的应用，将我们的建模方法与 LGCP 的预测性能进行了比较。我们的结果表明，当疾病风险被假设为一个空间连续的过程时，所提出的 LGCP 逼近方法可以在空间连续和聚集的尺度上可靠地估计疾病风险。所提出的方法已在开源 R 包 SDALGCP 中实现。
