Int J Health Geogr - Data-driven inference for the spatial scan statistic.


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CKGROUND: 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.

Resumo Limpo

ckground kulldorff spatial scan statist aggreg area map search cluster case without specifi size number area geograph locat advanc statist signific test adjust multipl test inher procedur howev shown work adjust done even manner possibl cluster sizesresult modif propos usual infer test spatial scan statist incorpor addit inform size like cluster found new interpret result spatial scan statist done pose modifi infer question probabl null hypothesi reject origin observ case map like cluster size k take account like cluster size k found null hypothesi comparison question especi import pvalu comput usual infer process near alpha signific level regard correct decis base inferenceconclus practic procedur provid make accur infer like cluster found spatial scan statist

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