國家衛生研究院 NHRI:Item 3990099045/9595
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    题名: Spatial scan statistics for detection of multiple clusters with arbitrary shapes
    作者: Lin, PS;Kung, YH;Clayton, M
    贡献者: Division of Biostatistics and Bioinformatics
    摘要: 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.
    日期: 2016-12
    關聯: Biometrics. 2016 Dec;72(4):1226-1234.
    Link to: http://dx.doi.org/10.1111/biom.12509
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0006-341X&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000391932100022
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84960154040
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