基于数据驱动的空间扫描统计推断。
Data-driven inference for the spatial scan statistic.
机构信息
Campus Alto Paraopeba, Universidade Federal de São João del Rei, Ouro Branco/MG, Brazil.
出版信息
Int J Health Geogr. 2011 Aug 2;10:47. doi: 10.1186/1476-072X-10-47.
BACKGROUND
Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes.
RESULTS
A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference.
CONCLUSIONS
A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.
背景
库尔多夫的聚集区域地图空间扫描统计量在事先不指定病例集群的大小（区域数量）或地理位置的情况下搜索病例集群。在对这种程序固有的多次检验进行调整的同时，对其统计显著性进行检验。然而，正如本工作所示，对于所有可能的集群大小，这种调整并非以均匀的方式进行。
结果
针对空间扫描统计量的常用推断检验提出了一种修正方法，纳入了关于所发现最大可能集群大小的额外信息。对空间扫描统计量的结果进行了新的解释，提出了一个修正的推断问题：在考虑仅根据零假设找到的大小为 k 的最大可能集群的情况下，对于原始观察到的病例地图，对于大小为 k 的最大可能集群，零假设被拒绝的概率是多少？当通常的推断过程计算出的 p 值接近α显著性水平时，这个问题尤其重要，这涉及到基于这种推断的决策的正确性。
结论
为空间扫描统计量发现的最大可能集群提供了更准确的推断方法。
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