The spatial scan statistic is a widely applied tool for cluster detection. The spatial scan statistic evaluates the significance of a series of potential circular clusters using Monte Carlo simulation to account for the multiplicity of comparisons. In most settings, the extent of the multiplicity problem varies across the study region. For example, urban areas typically have many overlapping clusters, while rural areas have few. The spatial scan statistic does not account for these local variations in the multiplicity problem. We propose two new spatially-varying multiplicity adjustments for spatial cluster detection, one based on a nested Bonferroni adjustment and one based on local averaging. Geographic variations in power for the spatial scan statistic and the two new statistics are explored through simulation studies, and the methods are applied to both the well-known New York leukemia data and data from a case–control study of breast cancer in Wisconsin.

中文翻译：

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空间聚类检测的局部多重性调整。

空间扫描统计是一种广泛应用的聚类检测工具。空间扫描统计使用蒙特卡罗模拟来评估一系列潜在圆形集群的重要性，以说明比较的多样性。在大多数情况下，多样性问题的程度在研究区域内各不相同。例如，城市地区通常有许多重叠的集群，而农村地区则很少。空间扫描统计没有考虑多重性问题中的这些局部变化。我们为空间集群检测提出了两种新的空间变化多重性调整，一种基于嵌套 Bonferroni 调整，另一种基于局部平均。通过模拟研究探索空间扫描统计和两个新统计的功率的地理变化，