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A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance
DC Field | Value | Language |
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dc.contributor.author | 정인경 | - |
dc.contributor.author | 박고은 | - |
dc.date.accessioned | 2022-12-22T04:18:44Z | - |
dc.date.available | 2022-12-22T04:18:44Z | - |
dc.date.issued | 2022-09 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/192100 | - |
dc.description.abstract | After new drugs enter the market, adverse events (AE) induced by their use must be tracked; rare AEs may not be detected during clinical trials. Some organizations have been collecting information on suspected drugs and AEs via a spontaneous reporting system to conduct post-market drug safety surveillance. These organizations use the information to detect a signal representing potential causality between drugs and AEs. The drug and AE data are often hierarchically structured. Accordingly, the tree-based scan statistic can be used as a statistical data mining method for signal detection. Most of the AE databases contain a large number of zero-count cells. Notably, not only an observational zero from the Poisson distribution, but also a true zero exists in zero-count cells. True zeros represent theoretically impossible observations or possible but unreported observations. The existing tree-based scan statistic assumes that all zeros are zero-valued observations from the Poisson distribution. Therefore, true zeros are not considered in the modeling, which can lead to bias in the inferences. In this study, we propose a tree-based scan statistic for zero-inflated count data in a hierarchical structure. According to our simulation study, in the presence of excess zeros, our proposed tree-based scan statistic provides better performance than the existing tree-based scan statistic. The two methods were illustrated using Korea Adverse Event Reporting System data from the Korea Institute of Drug Safety and Risk Management. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Academies and Institutes | - |
dc.subject.MESH | Computer Simulation | - |
dc.subject.MESH | Data Mining | - |
dc.subject.MESH | Insufflation* | - |
dc.subject.MESH | Radionuclide Imaging | - |
dc.title | A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) | - |
dc.contributor.googleauthor | Goeun Park | - |
dc.contributor.googleauthor | Inkyung Jung | - |
dc.identifier.doi | 10.1038/s41598-022-19998-5 | - |
dc.contributor.localId | A03693 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 36175526 | - |
dc.contributor.alternativeName | Jung, In Kyung | - |
dc.contributor.affiliatedAuthor | 정인경 | - |
dc.citation.volume | 12 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 16299 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.12(1) : 16299, 2022-09 | - |
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