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Spatial scan statistics for detection of multiple clusters with arbitrary shapes

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  • Pei‐Sheng Lin
  • Yi‐Hung Kung
  • Murray Clayton

Abstract

In applying scan statistics for public health research, it would be valuable to develop a detection method for multiple clusters that accommodates spatial correlation and covariate effects in an integrated model. In this article, we connect the concepts of the likelihood ratio (LR) scan statistic and the quasi‐likelihood (QL) scan statistic to provide a series of detection procedures sufficiently flexible to apply to clusters of arbitrary shape. First, we use an independent scan model for detection of clusters and then a variogram tool to examine the existence of spatial correlation and regional variation based on residuals of the independent scan model. When the estimate of regional variation is significantly different from zero, a mixed QL estimating equation is developed to estimate coefficients of geographic clusters and covariates. We use the Benjamini–Hochberg procedure (1995) to find a threshold for p‐values to address the multiple testing problem. A quasi‐deviance criterion is used to regroup the estimated clusters to find geographic clusters with arbitrary shapes. We conduct simulations to compare the performance of the proposed method with other scan statistics. For illustration, the method is applied to enterovirus data from Taiwan.

Suggested Citation

  • Pei‐Sheng Lin & Yi‐Hung Kung & Murray Clayton, 2016. "Spatial scan statistics for detection of multiple clusters with arbitrary shapes," Biometrics, The International Biometric Society, vol. 72(4), pages 1226-1234, December.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1226-1234
    DOI: 10.1111/biom.12509
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    References listed on IDEAS

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    1. Pei-Sheng Lin, 2014. "Generalized Scan Statistics for Disease Surveillance," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 791-808, September.
    2. Tonglin Zhang & Ge Lin, 2009. "Cluster Detection Based on Spatial Associations and Iterated Residuals in Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 65(2), pages 353-360, June.
    3. Daniel B. Neill, 2012. "Fast subset scan for spatial pattern detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 337-360, March.
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    Cited by:

    1. Pei‐Sheng Lin & Jun Zhu, 2020. "A heterogeneity measure for cluster identification with application to disease mapping," Biometrics, The International Biometric Society, vol. 76(2), pages 403-413, June.
    2. Kunihiko Takahashi & Hideyasu Shimadzu, 2018. "Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.

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