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The spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection for more than a decade. However, overdispersion often presents in real-world data, causing not only violation of the Poisson assumption but also excessive type I errors or false alarms. In order to account for overdispersion, we extend the Poisson-based spatial scan test to a quasi-Poisson-based test. The simulation shows that the proposed method can substantially reduce type I error probabilities in the presence of overdispersion. In a case study of infant mortality in Jiangxi, China, both tests detect a cluster; however, a secondary cluster is identified by only the Poisson-based test. It is recommended that a cluster detected by the Poisson-based scan test should be interpreted with caution when it is not confirmed by the quasi-Poisson-based test. Copyright © 2011 John Wiley & Sons, Ltd.
Department of Statistics, Purdue University, 250 North UniversityStreet, West Lafayette, IN 47907-2066, USA. firstname.lastname@example.org.
This article was published in the following journal.
Name: Statistics in medicine
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Methods used to take into account and incorporate spatial autocorrelation and regional variation into regression analysis models of data that has spatial dependency, and also to provide information on the spatial relationships among the variables.
The use of combination of imaging techniques or platforms (e.g., MRI SCAN and PET SCAN) encompassing aspects of anatomical, functional, or molecular imaging methods.
Used for general articles concerning statistics of births, deaths, marriages, etc.
Integration of spatial information perceived by visual and/or auditory CUES.
A process through which individuals encode information about their environmental CUES to facilitate SPATIAL NAVIGATION.