Abstract
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.
Highlights
Evaluating whether a disease is randomly distributed or tends to occur as clusters over space is among the most crucial aspects of epidemiological studies, and this can be primarily performed using disease mapping
We introduce a new R package rflexscan (Otani and Takahashi 2021), that is an R implementation of the FleXScan software for performing purely spatial analysis using the flexible scan statistic more efficiently
In addition to the flexible scan statistic, the rflexscan package implements the original spatial scan statistic proposed by Kulldorff and Nagarwalla (1995) and Kulldorff (1997) that considers only the set of circular-shaped windows Wc
Summary
Evaluating whether a disease is randomly distributed or tends to occur as clusters over space is among the most crucial aspects of epidemiological studies, and this can be primarily performed using disease mapping. Tango and Takahashi (2012) further proposed a flexible scan statistic with a restricted likelihood ratio that consumes much less computation time than the original one and tends to detect clusters of any shape reasonably well as the relative risk of the cluster increases. There is no R package that can efficiently conduct tests based on the flexible scan statistic, a stand-alone application, FleXScan (Takahashi, Yokoyama, and Tango 2013), is freely available. We introduce a new R package rflexscan (Otani and Takahashi 2021), that is an R implementation of the FleXScan software for performing purely spatial analysis using the flexible scan statistic more efficiently. The feasibility of the package is demonstrated through an application to the public domain small-area cancer incidence data for New York State during 2005–2009 (Boscoe et al 2016), which are available at https://www.satscan.org/datasets/nyscancer/.
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