The spatial scan statistic is a popular statistical tool to detect geographical clusters of diseases. The basic problem of constructing confidence intervals for the relative risk of the most likely cluster has remained an open question. To cover this lack, a Monte Carlo based interval estimator for the relative risk of the primary cluster is derived. The method works for the circular spatial scan statistic applied to binomial data, and it ensures, by construction, an analytical control of the coverage probability under the nominal confidence coefficient. In addition, its performance is illustrated on simulated and real data of birth defects in New York State.

空间扫描统计数据是检测疾病地理集群的一种流行统计工具。构建最可能的集群的相对风险的置信区间的基本问题仍然是一个悬而未决的问题。为了弥补这一不足，推导了基于蒙特卡洛的区间估计量，用于主要聚类的相对风险。该方法适用于应用于二项式数据的圆形空间扫描统计量，并且通过构造确保了在名义置信系数下对覆盖概率的解析控制。此外，其性能在纽约州出生缺陷的模拟和真实数据中得到了说明。