IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0221225.html
   My bibliography  Save this article

Spatial scan statistics for matched case-control data

Author

Listed:
  • Inkyung Jung

Abstract

Spatial scan statistics are widely used for cluster detection analysis in geographical disease surveillance. While this method has been developed for various types of data such as binary, count, and continuous data, spatial scan statistics for matched case-control data, which often arise in spatial epidemiology, have not been considered. We propose spatial scan statistics for matched case-control data. The proposed test statistics consider the correlations between matched pairs. We evaluate the statistical power and cluster detection accuracy of the proposed methods through simulations compared to the Bernoulli-based method. We illustrate the proposed methods using a real data example. The simulation study clearly revealed that the proposed methods had higher power and higher accuracy for detecting spatial clusters for matched case-control data than the Bernoulli-based spatial scan statistic. The cluster detection result of the real data example also appeared to reflect a higher power of the proposed methods. The proposed methods are very useful for spatial cluster detection for matched case-control data.

Suggested Citation

  • Inkyung Jung, 2019. "Spatial scan statistics for matched case-control data," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-10, August.
    • Handle: RePEc:plo:pone00:0221225
    DOI: 10.1371/journal.pone.0221225
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221225
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0221225&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0221225?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Costa, Marcelo Azevedo & Assunção, Renato Martins & Kulldorff, Martin, 2012. "Constrained spanning tree algorithms for irregularly-shaped spatial clustering," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1771-1783.
    2. Huang, Lan & Tiwari, Ram C. & Zou, Zhaohui & Kulldorff, Martin & Feuer, Eric J., 2009. "Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 886-898.
    3. 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.
    4. 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.
    5. 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.
    6. Duczmal, Luiz & Cancado, Andre L.F. & Takahashi, Ricardo H.C. & Bessegato, Lupercio F., 2007. "A genetic algorithm for irregularly shaped spatial scan statistics," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 43-52, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Silva, Ivair R. & Duczmal, Luiz & Kulldorff, Martin, 2021. "Confidence intervals for spatial scan statistic," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    2. 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.
    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. Andrea J. Cook & Yi Li & David Arterburn & Ram C. Tiwari, 2010. "Spatial Cluster Detection for Weighted Outcomes Using Cumulative Geographic Residuals," Biometrics, The International Biometric Society, vol. 66(3), pages 783-792, September.
    5. Wan, You & Pei, Tao & Zhou, Chenghu & Jiang, Yong & Qu, Chenxu & Qiao, Youlin, 2012. "ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 283-296.
    6. de Lima, Max Sousa & Duczmal, Luiz Henrique, 2014. "Adaptive likelihood ratio approaches for the detection of space–time disease clusters," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 352-370.
    7. Liu, Ying & Liu, Yawen & Zhang, Tonglin, 2018. "Wald-based spatial scan statistics for cluster detection," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 298-310.
    8. 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).
    9. Mohamed-Salem Ahmed & Lionel Cucala & Michaël Genin, 2021. "Spatial autoregressive models for scan statistic," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-20, December.
    10. Chadoeuf, J. & Certain, G. & Bellier, E. & Bar-Hen, A. & Couteron, P. & Monestiez, P. & Bretagnolle, V., 2011. "Estimating inter-group interaction radius for point processes with nested spatial structures," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 627-640, January.
    11. Fitzpatrick, Dylan & Ni, Yun & Neill, Daniel B., 2021. "Support vector subset scan for spatial pattern detection," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    12. Ali Abolhassani & Marcos O. Prates & Safieh Mahmoodi, 2023. "Irregular Shaped Small Nodule Detection Using a Robust Scan Statistic," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 141-162, April.
    13. Ran Zhang & Jing Li & Qingyun Du & Fu Ren, 2015. "Basic farmland zoning and protection under spatial constraints with a particle swarm optimisation multiobjective decision model: a case study of Yicheng, China," Environment and Planning B, , vol. 42(6), pages 1098-1123, November.
    14. 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.
    15. Margherita Silan & Pietro Belloni & Giovanna Boccuzzo, 2023. "Identification of neighborhood clusters on data balanced by a poset-based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1295-1316, October.
    16. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    17. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
    18. Xing Zhao & Xiao-Hua Zhou & Zijian Feng & Pengfei Guo & Hongyan He & Tao Zhang & Lei Duan & Xiaosong Li, 2013. "A Scan Statistic for Binary Outcome Based on Hypergeometric Probability Model, with an Application to Detecting Spatial Clusters of Japanese Encephalitis," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-7, June.
    19. Xiaolan Wu & Tony Grubesic, 2010. "Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm," Journal of Geographical Systems, Springer, vol. 12(4), pages 409-433, December.
    20. Zhou, Ruoyu & Shu, Lianjie & Su, Yan, 2015. "An adaptive minimum spanning tree test for detecting irregularly-shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 134-146.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0221225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.