Cited 6 times in
Optimizing the maximum reported cluster size in the spatial scan statistic for survival data
DC Field | Value | Language |
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dc.contributor.author | 정인경 | - |
dc.date.accessioned | 2021-09-29T01:36:21Z | - |
dc.date.available | 2021-09-29T01:36:21Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/184453 | - |
dc.description.abstract | Background: The spatial scan statistic is a useful tool for cluster detection analysis in geographical disease surveillance. The method requires users to specify the maximum scanning window size or the maximum reported cluster size (MRCS), which is often set to 50% of the total population. It is important to optimize the maximum reported cluster size, keeping the maximum scanning window size at as large as 50% of the total population, to obtain valid and meaningful results. Results: We developed a measure, a Gini coefficient, to optimize the maximum reported cluster size for the exponential-based spatial scan statistic. The simulation study showed that the proposed method mostly selected the optimal MRCS, similar to the true cluster size. The detection accuracy was higher for the best chosen MRCS than at the default setting. The application of the method to the Korea Community Health Survey data supported that the proposed method can optimize the MRCS in spatial cluster detection analysis for survival data. Conclusions: Using the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | BioMed Central | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Optimizing the maximum reported cluster size in the spatial scan statistic for survival data | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) | - |
dc.contributor.googleauthor | Sujee Lee | - |
dc.contributor.googleauthor | Jisu Moon | - |
dc.contributor.googleauthor | Inkyung Jung | - |
dc.identifier.doi | 10.1186/s12942-021-00286-w | - |
dc.contributor.localId | A03693 | - |
dc.relation.journalcode | J01119 | - |
dc.identifier.eissn | 1476-072X | - |
dc.identifier.pmid | 34238302 | - |
dc.subject.keyword | Exponential model | - |
dc.subject.keyword | Gini coefficient | - |
dc.subject.keyword | SaTScan | - |
dc.subject.keyword | Spatial cluster detection | - |
dc.contributor.alternativeName | Jung, In Kyung | - |
dc.contributor.affiliatedAuthor | 정인경 | - |
dc.citation.volume | 20 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 33 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS, Vol.20(1) : 33, 2021-07 | - |
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