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Modified spatial scan statistics using a restricted likelihood ratio for ordinal outcome data

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  • Lee, Myeonggyun
  • Jung, Inkyung

Abstract

Spatial scan statistics are widely used as a technique to detect geographical disease clusters for different types of data. It has been pointed out that the Poisson-based spatial scan statistic tends to detect rather larger clusters by absorbing insignificant neighbors with non-elevated risks. We suspect that the spatial scan statistic for ordinal data may also have similar undesirable phenomena. In this paper, we propose to apply a restricted likelihood ratio to spatial scan statistics for ordinal outcome data to circumvent such a phenomenon. Through a simulation study, we demonstrated not only that original spatial scan statistics have the over-detection phenomenon but also that our proposed methods have reasonable or better performance compared with the original methods. We illustrated the proposed methods using a real data set from the 2014 Health Screening Program of Korea with the diagnosis results of normal, caution, suspected disease, and diagnosed with disease as an ordinal outcome.

Suggested Citation

  • Lee, Myeonggyun & Jung, Inkyung, 2019. "Modified spatial scan statistics using a restricted likelihood ratio for ordinal outcome data," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 28-39.
    • Handle: RePEc:eee:csdana:v:133:y:2019:i:c:p:28-39
    DOI: 10.1016/j.csda.2018.09.005
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    References listed on IDEAS

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    1. Jiyu Kim & Inkyung Jung, 2017. "Evaluation of the Gini Coefficient in Spatial Scan Statistics for Detecting Irregularly Shaped Clusters," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-13, January.
    2. Lan Huang & Martin Kulldorff & David Gregorio, 2007. "A Spatial Scan Statistic for Survival Data," Biometrics, The International Biometric Society, vol. 63(1), pages 109-118, March.
    3. Duczmal, Luiz & Assuncao, Renato, 2004. "A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 269-286, March.
    4. Sehwi Kim & Inkyung Jung, 2017. "Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-15, July.
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    Cited by:

    1. Silva, Ivair R. & Duczmal, Luiz & Kulldorff, Martin, 2021. "Confidence intervals for spatial scan statistic," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).

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