Jung Inkyung, Park Goeun
Department of Biostatistics, Yonsei University College of Medicine, Seoul, 120-752, Korea.
Stat Med. 2015 Feb 10;34(3):504-14. doi: 10.1002/sim.6347. Epub 2014 Oct 24.
Spatial scan statistics are widely applied to identify spatial clusters in geographic disease surveillance. To evaluate the statistical significance of detected clusters, Monte Carlo hypothesis testing is often used because the null distribution of spatial scan statistics is not known. A drawback of the method is that we have to increase the number of replications to obtain accurate p-values. Gumbel-based p-value approximations for spatial scan statistics have recently been proposed and evaluated for Poisson and Bernoulli models. In this study, we examine the use of a generalized extreme value distribution to approximate the null distribution of spatial scan statistics as well as the Gumbel distribution. Through simulation, p-value approximations using extreme value distributions for spatial scan statistics are assessed for multinomial and ordinal models in addition to Poisson and Bernoulli models.
空间扫描统计方法被广泛应用于地理疾病监测中以识别空间聚集。为评估检测到的聚集的统计显著性，常使用蒙特卡洛假设检验，因为空间扫描统计的零分布是未知的。该方法的一个缺点是我们必须增加重复次数以获得准确的p值。最近有人提出并评估了基于耿贝尔分布的空间扫描统计的p值近似方法，用于泊松模型和伯努利模型。在本研究中，我们研究使用广义极值分布来近似空间扫描统计的零分布以及耿贝尔分布。通过模拟，除了泊松模型和伯努利模型外，还评估了使用极值分布对多项模型和有序模型进行空间扫描统计的p值近似方法。
