Data-driven inference for the spatial scan statistic.

Abstract

Background: Kulldorff’s spatial scan statistic for aggregated area map s searches for cluster s of case s without specifying their size (numb er of areas) or geo graphic 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 don e 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 hypo thesis 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 un der 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 provide d to make more accurate inferences about the most likely cluster found by the spatial scan statistic.