Hierarchical multivariate directed acyclic graph autoregressive models for spatial diseases mapping.
Hierarchical multivariate directed acyclic graph autoregressive models for spatial diseases mapping.
复制标题
用于空间疾病映射的分层多元有向无环图自回归模型。
DOI：
10.1002/sim.9404
复制
发表时间：
2022
影响因子：
2
通讯作者：
Banerjee,Sudipto
中科院分区：
文献类型：
--
作者：
Gao,Leiwen;Datta,Abhirup;Banerjee,Sudipto
疾病绘图是流行病学家用来评估疾病发病率的地理差异并从空间模式中识别潜在环境风险因素的重要统计工具。此类地图依赖于区域聚合数据的空间模型，其中邻近区域往往比相距较远的区域表现出相似的结果。我们为多变量疾病绘图的文献做出贡献，该文献涉及每个区域的多种（两种或多种）疾病的测量。我们的目标是将多种疾病之间的关联与每种疾病的空间自相关性分开。我们开发了多元定向非循环图形自回归模型来适应空间和疾病间依赖性。层次结构赋予灵活性和丰富性、空间自相关和疾病间关系的可解释性以及计算的简便性，但取决于癌症建模的顺序。为了避免这种情况，我们演示了如何使用桥采样轻松实现贝叶斯模型选择和跨阶平均。我们使用模拟研究将我们的方法与竞争对手的方法进行比较，并使用来自监测、流行病学和最终结果计划的数据提出了多种癌症绘图的应用。
Disease mapping is an important statistical tool used by epidemiologists to assess geographic variation in disease rates and identify lurking environmental risk factors from spatial patterns. Such maps rely upon spatial models for regionally aggregated data, where neighboring regions tend to exhibit similar outcomes than those farther apart. We contribute to the literature on multivariate disease mapping, which deals with measurements on multiple (two or more) diseases in each region. We aim to disentangle associations among the multiple diseases from spatial autocorrelation in each disease. We develop multivariate directed acyclic graphical autoregression models to accommodate spatial and inter‐disease dependence. The hierarchical construction imparts flexibility and richness, interpretability of spatial autocorrelation and inter‐disease relationships, and computational ease, but depends upon the order in which the cancers are modeled. To obviate this, we demonstrate how Bayesian model selection and averaging across orders are easily achieved using bridge sampling. We compare our method with a competitor using simulation studies and present an application to multiple cancer mapping using data from the Surveillance, Epidemiology, and End Results program.