Journal article
A spatial scan statistic for ordinal data
Statistics in medicine, v 26(7), pp 1594-1607
30 Mar 2007
PMID: 16795130
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Spatial scan statistics are widely used for count data to detect geographical disease clusters of high or low incidence, mortality or prevalence and to evaluate their statistical significance. Some data are ordinal or continuous in nature, however, so that it is necessary to dichotomize the data to use a traditional scan statistic for count data. There is then a loss of information and the choice of cut-off point is often arbitrary. In this paper, we propose a spatial scan statistic for ordinal data, which allows us to analyse such data incorporating the ordinal structure without making any further assumptions. The test statistic is based on a likelihood ratio test and evaluated using Monte Carlo hypothesis testing. The proposed method is illustrated using prostate cancer grade and stage data from the Maryland Cancer Registry. The statistical power, sensitivity and positive predicted value of the test are examined through a simulation study.
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Details
- Title
- A spatial scan statistic for ordinal data
- Creators
- Inkyung Jung - Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, 133 Brookline Ave. 6th Floor, Boston, MA 02215, USA. inkyung.jung@gmail.comMartin KulldorffAnn C Klassen
- Publication Details
- Statistics in medicine, v 26(7), pp 1594-1607
- Publisher
- Wiley; England
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Community Health and Prevention
- Web of Science ID
- WOS:000244903700013
- Scopus ID
- 2-s2.0-33947141920
- Other Identifier
- 991014878142704721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Medical Informatics
- Medicine, Research & Experimental
- Public, Environmental & Occupational Health
- Statistics & Probability