Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Cancer cluster detection using scan statistic

스캔 통계량을 이용한 암 클러스터 탐색

  • Han, Junhee (Division of Biostatistics, Pusan National University Yangsan Hospital) ;
  • Lee, Minjung (Department of Statistics, Kangwon National University)
  • 한준희 (양산부산대학교병원 의학통계실) ;
  • 이민정 (강원대학교 정보통계학과)
  • Received : 2016.08.28
  • Accepted : 2016.09.21
  • Published : 2016.09.30
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Abstract

In epidemiology or etiology, we are often interested in identifying areas of elevated risk, so called, hot spot or cluster. Many existing clustering methods only tend to a result if there exists any clustering pattern in study area. Recently, however, lots of newly introduced clustering methods can identify the location, size, and shape of clusters and test if the clusters are statistically significant as well. In this paper, one of most commonly used clustering methods, scan statistic, and its implementation SaTScan software, which is freely available, will be introduced. To exemplify the usage of SaTScan software, we used cancer data from the SEER program of National Cancer Institute of U.S.A.We aimed to help researchers and practitioners, who are interested in spatial cluster detection, using female lung cancer mortality data of the SEER program.

공간 또는 시공간 데이터에서 다른 지역에 비해 유난히 높은 위험률을 보이는 소위 핫 스팟 (hot spot)으로 불리는 클러스터 (cluster)를 찾으려고 하는 경우가 많다. 기존의 많은 방법들은 이러한 클러스터 패턴이 존재하는지에 대한 해답만 주었지만, 최근의 많은 방법들은 클러스터의 위치, 모양, 크기뿐만 아니라 찾아진 클러스터가 통계적으로 유의한지까지 검정해준다. 본 논문에서는 이러한 다양한 방법 중 가장 많이 사용되는 클러스터 탐색 방법 중 하나인 스캔 통계량을 이용한 방법을 소개하고 그 방법이 구현된 무료 소프트웨어 SaTScan을 이용한 결과를 보여주고 장단점을 논하고자 한다. 미국 국립암센터의 SEER 프로그램에서 제공하는 미국의 각 카운티별 암 사망자 자료 중 2006년 여성 폐암 사망자 데이터를 예시 데이터로 사용하여 스캔 통계량을 이용하여 구한 클러스터 탐색 결과를 제시하고 비슷한 연구를 하고자는 연구자에게 도움을 주고자 한다.

Keywords

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