The analysis of tumorigenicity data using a frailty effect
DC Field | Value | Language |
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dc.contributor.author | Kim, Yang-Jin | - |
dc.contributor.author | Nam, Chung Mo | - |
dc.contributor.author | Kim, Youn Nam | - |
dc.contributor.author | Choi, Eun Hee | - |
dc.contributor.author | Kim, Jinheum | - |
dc.date.available | 2021-02-22T13:16:32Z | - |
dc.date.issued | 2011-09 | - |
dc.identifier.issn | 1226-3192 | - |
dc.identifier.issn | 1876-4231 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/12505 | - |
dc.description.abstract | In animal tumorigenicity data, the time of occurrence of the tumor is not observed because the existence of the tumor is looked for only at either the time of death or the time of sacrifice of the animal. Such an incomplete data structure makes it difficult to investigate the impact of treatment on the occurrence of tumors. A three-state model (no tumor-tumor-death) is used to model events that occurred sequentially and to connect them. In this paper, we also employed a frailty effect to model the dependency of death on tumor occurrence. For the inference of parameters, an EM algorithm is considered. The method is applied to a real bladder tumor data set and a simulation study is performed to show the behavior of the proposed estimators. (C) 2010 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | KOREAN STATISTICAL SOC | - |
dc.title | The analysis of tumorigenicity data using a frailty effect | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.doi | 10.1016/j.jkss.2010.10.007 | - |
dc.identifier.scopusid | 2-s2.0-79960300607 | - |
dc.identifier.wosid | 000293311700005 | - |
dc.identifier.bibliographicCitation | JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.40, no.3, pp 281 - 290 | - |
dc.citation.title | JOURNAL OF THE KOREAN STATISTICAL SOCIETY | - |
dc.citation.volume | 40 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 281 | - |
dc.citation.endPage | 290 | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART001589085 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | PROPORTIONAL HAZARDS MODEL | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | TIME | - |
dc.subject.keywordAuthor | Current status data | - |
dc.subject.keywordAuthor | Frailty effect | - |
dc.subject.keywordAuthor | EM algorithm | - |
dc.subject.keywordAuthor | Three-state model | - |
dc.subject.keywordAuthor | Tumorigenicity | - |
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