The spatial scan statistic is a useful tool for cluster detection analysis in geographical disease surveillance. The method requires users to specify the maximum scanning window size or the maximum reported cluster size (MRCS), which is often set to 50% of the total population. It is important to optimize the maximum reported cluster size, keeping the maximum scanning window size at as large as 50% of the total population, to obtain valid and meaningful results. We developed a measure, a Gini coefficient, to optimize the maximum reported cluster size for the exponential-based spatial scan statistic. The simulation study showed that the proposed method mostly selected the optimal MRCS, similar to the true cluster size. The detection accuracy was higher for the best chosen MRCS than at the default setting. The application of the method to the Korea Community Health Survey data supported that the proposed method can optimize the MRCS in spatial cluster detection analysis for survival data. Using the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data.

中文翻译：

---

优化生存数据空间扫描统计中报告的最大簇大小

空间扫描统计是地理疾病监测中聚类检测分析的有用工具。该方法要求用户指定最大扫描窗口大小或最大报告簇大小 (MRCS)，通常设置为总人口的 50%。重要的是优化报告的最大簇大小，将最大扫描窗口大小保持为总人口的 50%，以获得有效和有意义的结果。我们开发了一个度量，一个基尼系数，以优化基于指数的空间扫描统计的最大报告集群大小。仿真研究表明，所提出的方法大多选择了最佳的 MRCS，与真实的簇大小相似。与默认设置相比，最佳选择的 MRCS 的检测精度更高。该方法在韩国社区健康调查数据中的应用表明，该方法可以优化MRCS在生存数据空间聚类检测分析中的应用。在基于指数的空间扫描统计中使用基尼系数对于报告生存数据的更精细和信息丰富的集群非常有帮助。