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Journal of Probability and Statistics
Volume 2010, Article ID 642379, 11 pages
Research Article

Spatial Scan Statistics Adjusted for Multiple Clusters

1Personal Market - Property Strategic Research Team, Liberty Mutual Group, 175 Berkeley Street 10GH, Boston, MA 02116-4715, USA
2Departamento de Estatística, Universidade Federal de Minas Gerais, 31270-901 Belo Horizonte, MG, Brazil
3Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, 133 Brookline Avenue, Boston, MA 02215, USA

Received 26 November 2009; Revised 24 March 2010; Accepted 9 June 2010

Academic Editor: Rongling Wu

Copyright © 2010 Zhenkui Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The spatial scan statistic is one of the main epidemiological tools to test for the presence of disease clusters in a geographical region. While the statistical significance of the most likely cluster is correctly assessed using the model assumptions, secondary clusters tend to have conservatively high P-values. In this paper, we propose a sequential version of the spatial scan statistic to adjust for the presence of other clusters in the study region. The procedure removes the effect due to the more likely clusters on less significant clusters by sequential deletion of the previously detected clusters. Using the Northeastern United States geography and population in a simulation study, we calculated the type I error probability and the power of this sequential test under different alternative models concerning the locations and sizes of the true clusters. The results show that the type I error probability of our method is close to the nominal level and that for secondary clusters its power is higher than the standard unadjusted scan statistic.