A study of methods for detecting multiple clusters

• Main Authors: Huang, Bo Cheng, 黃柏誠
• Other Authors: Jack C. Yue
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/53979672248076311884

碩士 === 國立政治大學 === 統計研究所 === 100 === Cluster detection, one of the major research topics in spatial statistics, has been applied to identify areas with higher incidence rates and is very popular in many fields such as epidemiology. Many famous cluster detection methods are proposed, such as SaTScan (Kulldorff, 1995) and Spatial Scan Statistic (Li et al., 2011). Most of these methods adapt the idea for comparing the relative risk inside and outside the suspected clusters. Although these methods are efficient computationally, clusters with smaller relative risk are not easy to be detected (Zhang et al, 2010). The goal of this study is to apply the idea of sequential search into SaTScan, in order to improve the power of detecting clusters with smaller relative risk, and to explore the limitation of sequential method. The computer simulation and empirical study (Taiwan cancer mortality data) are used to evaluate the sequential SaTScan. We found that the Sequential method can improve the power of cluster detection, especially effective for the cases where the clusters with relative risk not greater than 1.6. However, the sequential method also suffers from identifying false clusters.