Spatial Scan Statistics Adjusted for Multiple Clusters
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 h...
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Series: | Journal of Probability and Statistics |
Online Access: | http://dx.doi.org/10.1155/2010/642379 |
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doaj-971ad99c518f455e894fcf8c063c9c922020-11-24T22:52:01ZengHindawi LimitedJournal of Probability and Statistics1687-952X1687-95382010-01-01201010.1155/2010/642379642379Spatial Scan Statistics Adjusted for Multiple ClustersZhenkui Zhang0Renato Assunção1Martin Kulldorff2Personal Market - Property Strategic Research Team, Liberty Mutual Group, 175 Berkeley Street 10GH, Boston, MA 02116-4715, USADepartamento de Estatística, Universidade Federal de Minas Gerais, 31270-901 Belo Horizonte, MG, BrazilDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, 133 Brookline Avenue, Boston, MA 02215, USAThe 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 α level and that for secondary clusters its power is higher than the standard unadjusted scan statistic.http://dx.doi.org/10.1155/2010/642379 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhenkui Zhang Renato Assunção Martin Kulldorff |
spellingShingle |
Zhenkui Zhang Renato Assunção Martin Kulldorff Spatial Scan Statistics Adjusted for Multiple Clusters Journal of Probability and Statistics |
author_facet |
Zhenkui Zhang Renato Assunção Martin Kulldorff |
author_sort |
Zhenkui Zhang |
title |
Spatial Scan Statistics Adjusted for Multiple Clusters |
title_short |
Spatial Scan Statistics Adjusted for Multiple Clusters |
title_full |
Spatial Scan Statistics Adjusted for Multiple Clusters |
title_fullStr |
Spatial Scan Statistics Adjusted for Multiple Clusters |
title_full_unstemmed |
Spatial Scan Statistics Adjusted for Multiple Clusters |
title_sort |
spatial scan statistics adjusted for multiple clusters |
publisher |
Hindawi Limited |
series |
Journal of Probability and Statistics |
issn |
1687-952X 1687-9538 |
publishDate |
2010-01-01 |
description |
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 α level and that for secondary clusters its power is higher than the standard unadjusted scan statistic. |
url |
http://dx.doi.org/10.1155/2010/642379 |
work_keys_str_mv |
AT zhenkuizhang spatialscanstatisticsadjustedformultipleclusters AT renatoassuncao spatialscanstatisticsadjustedformultipleclusters AT martinkulldorff spatialscanstatisticsadjustedformultipleclusters |
_version_ |
1725667640411684864 |