Universidad CEU-Cardenal Herrera, Valencia, Spain.
Stat Med. 2013 Jul 10;32(15):2595-612. doi: 10.1002/sim.5704. Epub 2012 Dec 10.
This paper introduces spatial moving average risk smoothing (SMARS) as a new way of carrying out disease mapping. This proposal applies the moving average ideas of time series theory to the spatial domain, making use of a spatial moving average process of unknown order to define dependence on the risk of a disease occurring. Correlation of the risks for different locations will be a function of m values (m being unknown), providing a rich class of correlation functions that may be reproduced by SMARS. Moreover, the distance (in terms of neighborhoods) that should be covered for two units to be found to make the correlation of their risks 0 is a quantity to be fitted by the model. This way, we reproduce patterns that range from spatially independent to long-range spatially dependent. We will also show a theoretical study of the correlation structure induced by SMARS, illustrating the wide variety of correlation functions that this proposal is able to reproduce. We will also present three applications of SMARS to both simulated and real datasets. These applications will show SMARS to be a competitive disease mapping model when compared with alternative proposals that have already appeared in the literature. Finally, the application of SMARS to the study of mortality for 21 causes of death in the Comunitat Valenciana will allow us to identify some qualitative differences in the patterns of those diseases.
本文提出了空间移动平均风险平滑（SMARS）作为一种新的疾病制图方法。本研究将时间序列理论中的移动平均思想应用到空间域，利用未知阶数的空间移动平均过程来定义疾病发生风险的依赖性。不同位置的风险相关性将是 m 值的函数（m 是未知的），为 SMARS 提供了丰富的相关函数类，可以再现这些相关性。此外，两个单元的风险相关性为 0 时，它们之间应该覆盖的距离（以邻域为单位）是由模型拟合的一个量。这样，我们再现了从空间独立到长程空间相关的各种模式。我们还将对 SMARS 诱导的相关结构进行理论研究，说明该方法能够再现的广泛的相关函数。我们还将展示 SMARS 在模拟和真实数据集上的三个应用。与文献中已经出现的替代方法相比，这些应用将表明 SMARS 是一种具有竞争力的疾病制图模型。最后，将 SMARS 应用于研究瓦伦西亚社区（Comunitat Valenciana）的 21 种死亡原因的死亡率，将使我们能够识别出这些疾病模式的一些定性差异。
