title: Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.
abstract: Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set-proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.
authors.affiliation: West China School of Public Health, Sichuan University, Chengdu 610041, China.
authors.forename: Yue
authors.initials: Y
authors.lastname: Ma
authors.affiliation: West China School of Public Health, Sichuan University, Chengdu 610041, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Yin
authors.affiliation: West China School of Public Health, Sichuan University, Chengdu 610041, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics, School of Public Health, University of Washington, Seattle 98195, United States of America.
authors.forename: Xiaohua Andrew
authors.initials: XA
authors.lastname: Zhou
authors.affiliation: West China School of Public Health, Sichuan University, Chengdu 610041, China.
authors.forename: Xiaosong
authors.initials: X
authors.lastname: Li
mesh_terms: D001943:Breast Neoplasms / Q000401:mortality*; D002681:China / Q000453:epidemiology; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D008457:Measles / Q000453:epidemiology*
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