Department of Biostatistics, Yonsei University College of Medicine, Seoul, Korea.
Stat Med. 2010 Aug 15;29(18):1910-8. doi: 10.1002/sim.3951.
As a geographical cluster detection analysis tool, the spatial scan statistic has been developed for different types of data such as Bernoulli, Poisson, ordinal, exponential and normal. Another interesting data type is multinomial. For example, one may want to find clusters where the disease-type distribution is statistically significantly different from the rest of the study region when there are different types of disease. In this paper, we propose a spatial scan statistic for such data, which is useful for geographical cluster detection analysis for categorical data without any intrinsic order information. The proposed method is applied to meningitis data consisting of five different disease categories to identify areas with distinct disease-type patterns in two counties in the U.K. The performance of the method is evaluated through a simulation study.
作为一种地理聚类检测分析工具，空间扫描统计量已经针对不同类型的数据进行了开发，例如伯努利、泊松、有序、指数和正态分布。另一种有趣的数据类型是多项分布。例如，当存在不同类型的疾病时，人们可能希望找到疾病类型分布在统计学上与研究区域其他部分显著不同的聚类。在本文中，我们提出了一种针对此类数据的空间扫描统计量，它对于没有任何内在顺序信息的分类数据的地理聚类检测分析非常有用。该方法应用于由五种不同疾病类型组成的脑膜炎数据，以识别英国两个县中具有明显疾病类型模式的区域。通过模拟研究评估了该方法的性能。
