Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, Pamplona, Spain.
InaMat, Public University of Navarre, Pamplona, Spain.
Stat Methods Med Res. 2021 Jan;30(1):6-21. doi: 10.1177/0962280220946502.
Many statistical models have been developed during the last years to smooth risks in disease mapping. However, most of these modeling approaches do not take possible local discontinuities into consideration or if they do, they are computationally prohibitive or simply do not work when the number of small areas is large. In this paper, we propose a two-step method to deal with discontinuities and to smooth noisy risks in small areas. In a first stage, a novel density-based clustering algorithm is used. In contrast to previous proposals, this algorithm is able to automatically detect the number of spatial clusters, thus providing a single cluster structure. In the second stage, a Bayesian hierarchical spatial model that takes the cluster configuration into account is fitted, which accounts for the discontinuities in disease risk. To evaluate the performance of this new procedure in comparison to previous proposals, a simulation study has been conducted. Results show competitive risk estimates at a much better computational cost. The new methodology is used to analyze stomach cancer mortality data in Spanish municipalities.
近年来，已经开发出许多统计模型来平滑疾病制图中的风险。然而，这些建模方法中的大多数都没有考虑到可能存在的局部不连续性，或者如果考虑到了，它们在小区域数量较大时计算成本过高，或者根本无法工作。在本文中，我们提出了一种两步法来处理不连续性并平滑小区域中的噪声风险。在第一阶段，使用一种新颖的基于密度的聚类算法。与以前的提议不同，该算法能够自动检测空间聚类的数量，从而提供单个聚类结构。在第二阶段，拟合考虑到聚类配置的贝叶斯层次空间模型，该模型考虑了疾病风险的不连续性。为了评估与以前的提议相比，这种新方法的性能，进行了一项模拟研究。结果表明，在计算成本更低的情况下，竞争风险估计具有竞争力。该新方法用于分析西班牙市镇的胃癌死亡率数据。
