IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v158y2021ics0167947321000190.html
   My bibliography  Save this article

Confidence intervals for spatial scan statistic

Author

Listed:
  • Silva, Ivair R.
  • Duczmal, Luiz
  • Kulldorff, Martin

Abstract

The spatial scan statistic is a popular statistical tool to detect geographical clusters of diseases. The basic problem of constructing confidence intervals for the relative risk of the most likely cluster has remained an open question. To cover this lack, a Monte Carlo based interval estimator for the relative risk of the primary cluster is derived. The method works for the circular spatial scan statistic applied to binomial data, and it ensures, by construction, an analytical control of the coverage probability under the nominal confidence coefficient. In addition, its performance is illustrated on simulated and real data of birth defects in New York State.

Suggested Citation

  • Silva, Ivair R. & Duczmal, Luiz & Kulldorff, Martin, 2021. "Confidence intervals for spatial scan statistic," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
  • Handle: RePEc:eee:csdana:v:158:y:2021:i:c:s0167947321000190
    DOI: 10.1016/j.csda.2021.107185
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947321000190
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2021.107185?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Julian Besag & James Newell, 1991. "The Detection of Clusters in Rare Diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(1), pages 143-155, January.
    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. 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.
    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. 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.
    7. Kulldorff, Martin & Tango, Toshiro & Park, Peter J., 2003. "Power comparisons for disease clustering tests," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 665-684, April.
    8. 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.
    9. 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.
    10. Silva, Ivair R., 2017. "Confidence intervals through sequential Monte Carlo," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 112-124.
    11. Inkyung Jung, 2019. "Spatial scan statistics for matched case-control data," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-10, August.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Inkyung Jung, 2019. "Spatial scan statistics for matched case-control data," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-10, August.
    3. 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.
    4. 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.
    5. Demattei[diaeresis], Christophe & Molinari, Nicolas & Daures, Jean-Pierre, 2007. "Arbitrarily shaped multiple spatial cluster detection for case event data," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3931-3945, May.
    6. HAEDO, Christian & MOUCHART , Michel & ,, 2013. "Specialized agglomerations with areal data: model and detection," LIDAM Discussion Papers CORE 2013060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Mohammad Meysami & Joshua P. French & Ettie M. Lipner, 2023. "Flexible-Elliptical Spatial Scan Method," Mathematics, MDPI, vol. 11(17), pages 1-22, August.
    12. Porter, Michael D. & Brown, Donald E., 2007. "Detecting local regions of change in high-dimensional criminal or terrorist point processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2753-2768, February.
    13. Rhonda J. Rosychuk & Carolyn Huston & Narasimha G. N. Prasad, 2006. "Spatial Event Cluster Detection Using a Compound Poisson Distribution," Biometrics, The International Biometric Society, vol. 62(2), pages 465-470, June.
    14. 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.
    15. 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.
    16. Zhanjun He & Rongqi Lai & Zhipeng Wang & Huimin Liu & Min Deng, 2022. "Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics," IJERPH, MDPI, vol. 19(21), pages 1-16, November.
    17. Fitzpatrick, Dylan & Ni, Yun & Neill, Daniel B., 2021. "Support vector subset scan for spatial pattern detection," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    18. Trisalyn Nelson & Barry Boots, 2005. "Identifying insect infestation hot spots: an approach using conditional spatial randomization," Journal of Geographical Systems, Springer, vol. 7(3), pages 291-311, December.
    19. Johnston, Robert J. & Ramachandran, Mahesh & Schultz, Eric T. & Segerson, Kathleen & Besedin, Elena Y., 2011. "Characterizing Spatial Pattern in Ecosystem Service Values when Distance Decay Doesn’t Apply: Choice Experiments and Local Indicators of Spatial Association," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103374, Agricultural and Applied Economics Association.
    20. Ikuho Yamada & Peter Rogerson & Gyoungju Lee, 2009. "GeoSurveillance: a GIS-based system for the detection and monitoring of spatial clusters," Journal of Geographical Systems, Springer, vol. 11(2), pages 155-173, June.

    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:eee:csdana:v:158:y:2021:i:c:s0167947321000190. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.