Cited 4 times in
Modified spatial scan statistics using a restricted likelihood ratio for ordinal outcome data
DC Field | Value | Language |
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dc.contributor.author | 정인경 | - |
dc.date.accessioned | 2019-05-29T05:08:41Z | - |
dc.date.available | 2019-05-29T05:08:41Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/169423 | - |
dc.description.abstract | Spatial scan statistics are widely used as a technique to detect geographical disease clusters for different types of data. It has been pointed out that the Poisson-based spatial scan statistic tends to detect rather larger clusters by absorbing insignificant neighbors with non-elevated risks. We suspect that the spatial scan statistic for ordinal data may also have similar undesirable phenomena. In this paper, we propose to apply a restricted likelihood ratio to spatial scan statistics for ordinal outcome data to circumvent such a phenomenon. Through a simulation study, we demonstrated not only that original spatial scan statistics have the over-detection phenomenon but also that our proposed methods have reasonable or better performance compared with the original methods. We illustrated the proposed methods using a real data set from the 2014 Health Screening Program of Korea with the diagnosis results of normal, caution, suspected disease, and diagnosed with disease as an ordinal outcome. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Elsevier B.V. | - |
dc.relation.isPartOf | COMPUTATIONAL STATISTICS & DATA ANALYSIS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.title | Modified spatial scan statistics using a restricted likelihood ratio for ordinal outcome data | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) | - |
dc.contributor.googleauthor | Myeonggyun Lee | - |
dc.contributor.googleauthor | Inkyung Jung | - |
dc.identifier.doi | 10.1016/j.csda.2018.09.005 | - |
dc.contributor.localId | A03693 | - |
dc.relation.journalcode | J00635 | - |
dc.identifier.eissn | 1872-7352 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0167947318302330 | - |
dc.contributor.alternativeName | Jung, In Kyung | - |
dc.contributor.affiliatedAuthor | 정인경 | - |
dc.citation.volume | 133 | - |
dc.citation.startPage | 28 | - |
dc.citation.endPage | 39 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL STATISTICS & DATA ANALYSIS, Vol.133 : 28-39, 2019 | - |
dc.identifier.rimsid | 62165 | - |
dc.type.rims | ART | - |
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