A spatial scan statistic for ordinal data

Inkyung Jung, Martin Kulldorff, Ann C. Klassen

Research output: Contribution to journalArticlepeer-review

75 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1594-1607
Number of pages14
JournalStatistics in Medicine
Issue number7
StatePublished - Mar 30 2007
Externally publishedYes


  • Clusters
  • Geographical disease surveillance
  • Prostate cancer

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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