用于检测空间疾病簇的灵活扫描统计:rflexscan R包

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Takahiro Otani, Kunihiko Takahashi
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引用次数: 7

摘要

空间扫描统计量在流行病学研究中常用来检测空间疾病聚集。在各种类型的扫描统计量中,Tango和Takahashi(2005)提出的柔性扫描统计量是最有希望检测任意形状聚类的方法之一。在本文中,我们介绍了一个新的R包,rflexscan (Otani和Takahashi 2021),它提供了使用灵活的空间扫描统计分析空间计数数据的高效和易于使用的方法。该一揽子方案是为下列任何相互关联的目的而设计的:评估所报告的空间疾病群集是否具有统计显著性,检验一种疾病是否在空间上随机分布,以及对疾病进行地理监测,以发现发病率极高的地区。通过对2005年至2009年期间美国纽约州公共领域小区域癌症发病率数据集的应用程序演示了该软件包的功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flexible Scan Statistics for Detecting Spatial Disease Clusters: The rflexscan R Package
The spatial scan statistic is commonly used to detect spatial disease clusters in epidemiological studies. Among the various types of scan statistics, the flexible scan statistic proposed by Tango and Takahashi (2005) is one of the most promising methods to detect arbitrarily-shaped clusters. In this paper, we introduce a new R package, rflexscan (Otani and Takahashi 2021), that provides efficient and easy-to-use methods for analyses of spatial count data using the flexible spatial scan statistic. The package is designed for any of the following interrelated purposes: to evaluate whether reported spatial disease clusters are statistically significant, to test whether a disease is randomly distributed over space, and to perform geographical surveillance of disease to detect areas of significantly high rates. The functionality of the package is demonstrated through an application to a public-domain small-area cancer incidence dataset in New York State, USA, between 2005 and 2009.
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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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