Tango T
Division of Theoretical Epidemiology, Department of Epidemiology, The Institute of Public Health, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108, Japan.
Stat Med. 2000 Jan 30;19(2):191-204. doi: 10.1002/(sici)1097-0258(20000130)19:2<191::aid-sim281>3.0.co;2-q.
Tango (1995) proposed a test statistic C for detecting spatial disease clusters. However, like most other methods, it requires a value of the scale parameter which adjusts for the size of cluster to be detected in advance of its use. When an appropriate size of cluster cannot be predicted and many clustered areas are expected, we usually repeat the procedure by changing the size parameter, but this practice clearly faces a multiple testing problem. In this paper, to ameliorate this problem, we propose an extended test statistic which searches the minimum of the profile P-value of C for the parameter on cluster size which varies continuously from a small value near zero upwards until it reaches to about half the size of the whole study area. Monte Carlo simulation study shows that the power of the proposed method is shown to be reasonably high compared with the best power attained by C unadjusted for multiple testing in all the cluster models considered. The proposed procedure is illustrated with some disease maps simulated in the Tokyo Metropolitan Area.
坦戈（1995年）提出了一种用于检测空间疾病聚集的检验统计量C。然而，与大多数其他方法一样，它需要一个尺度参数值，该参数需在使用前根据要检测的聚集大小进行调整。当无法预测合适的聚集大小时，且预计存在多个聚集区域时，我们通常会通过改变大小参数来重复该过程，但这种做法显然面临多重检验问题。在本文中，为改善这一问题，我们提出了一种扩展的检验统计量，它会在聚集大小参数上搜索C的轮廓P值的最小值，该参数从接近零的小值开始连续变化，直至达到整个研究区域大小的约一半。蒙特卡罗模拟研究表明，在所考虑的所有聚集模型中，与未针对多重检验进行调整的C所能达到的最佳功效相比，所提出方法的功效显示出相当高。通过在东京都市区模拟的一些疾病地图对所提出的程序进行了说明。
