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Spatial Scan Statistics Adjusted for Multiple Clusters

Author

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  • Zhenkui Zhang
  • Renato Assunção
  • Martin Kulldorff

Abstract

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 level and that for secondary clusters its power is higher than the standard unadjusted scan statistic.

Suggested Citation

  • Zhenkui Zhang & Renato Assunção & Martin Kulldorff, 2010. "Spatial Scan Statistics Adjusted for Multiple Clusters," Journal of Probability and Statistics, Hindawi, vol. 2010, pages 1-11, August.
    • Handle: RePEc:hin:jnljps:642379
    DOI: 10.1155/2010/642379
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    Cited by:

    1. Smida, Zaineb & Cucala, Lionel & Gannoun, Ali & Durif, Ghislain, 2022. "A Wilcoxon-Mann-Whitney spatial scan statistic for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    2. Julie Le Gallo & Fernando A. López & Coro Chasco, 2020. "Testing for spatial group-wise heteroskedasticity in spatial autocorrelation regression models: Lagrange multiplier scan tests," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 287-312, April.
    3. Wei Wang & Sheng Li & Tao Zhang & Fei Yin & Yue Ma, 2023. "Detecting the spatial clustering of exposure–response relationships with estimation error: a novel spatial scan statistic," Biometrics, The International Biometric Society, vol. 79(4), pages 3522-3532, December.
    4. Maddah, Lina & Arauzo Carod, Josep Maria & López, Fernando A., 2021. "Clusters of Cultural and Creative Industries: Empirical Evidence for Catalonia," Working Papers 2072/534911, Universitat Rovira i Virgili, Department of Economics.
    5. Smida, Zaineb & Laurent, Thibault & Cucala, Lionel, 2024. "A Hotelling spatial scan statistic for functional data: application to economic and climate data," TSE Working Papers 24-1583, Toulouse School of Economics (TSE).

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