Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI 53726, USA.
Department of Statistical Science, Baylor University, Waco, TX 76798, USA.
Spat Spatiotemporal Epidemiol. 2022 Jun;41:100462. doi: 10.1016/j.sste.2021.100462. Epub 2021 Nov 1.
Spatial and spatio-temporal cluster detection are important tools in public health and many other areas of application. Cluster detection can be approached as a multiple testing problem, typically using a space and time scan statistic. We recast the spatial and spatio-temporal cluster detection problem in a high-dimensional data analytical framework with Poisson or quasi-Poisson regression with the Lasso penalty. We adopt a fast and computationally-efficient method using a novel sparse matrix representation of the effects of potential clusters. The number of clusters and tuning parameters are selected based on (quasi-)information criteria. We evaluate the performance of our proposed method including the false positive detection rate and power using a simulation study. Application of the method is illustrated using breast cancer incidence data from three prefectures in Japan.
空间和时空聚类检测是公共卫生和许多其他应用领域的重要工具。聚类检测可以作为一个多重检验问题来处理，通常使用空间和时间扫描统计量。我们将空间和时空聚类检测问题重新构建为具有泊松或拟泊松回归和 Lasso 惩罚的高维数据分析框架。我们采用一种快速且计算效率高的方法，使用潜在聚类效应的新稀疏矩阵表示。基于（拟）信息准则选择聚类的数量和调整参数。我们使用模拟研究评估了我们提出的方法的性能，包括假阳性检出率和功效。该方法的应用通过来自日本三个县的乳腺癌发病率数据进行说明。
