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Joint Model of Clustered Failure Time Data with Informative Cluster Size

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dc.contributor.authorYoo, Hanna-
dc.contributor.authorLee, Jaewon-
dc.contributor.authorKim, Yang-Jin-
dc.date.available2021-02-22T13:32:38Z-
dc.date.issued2011-07-
dc.identifier.issn0361-0918-
dc.identifier.issn1532-4141-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/13020-
dc.description.abstractIn this article, we propose an estimation procedure to estimate parameters of joint model when there exists a relationship between cluster size and clustered failure times of subunits within a cluster. We use a joint random effect model of clustered failure times and cluster size. To investigate the possible association, two submodels are connected by a common latent variable. The EM algorithm is applied for the estimation of parameters in the models. Simulation studies are performed to assess the finite sample properties of the estimators. Also, sensitivity tests show the influence of the misspecification of random effect distributions. The methods are applied to a lymphatic filariasis study for adult worm nests.-
dc.format.extent10-
dc.language영어-
dc.language.isoENG-
dc.publisherTAYLOR & FRANCIS INC-
dc.titleJoint Model of Clustered Failure Time Data with Informative Cluster Size-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1080/03610918.2011.556289-
dc.identifier.scopusid2-s2.0-79952968513-
dc.identifier.wosid000288678500002-
dc.identifier.bibliographicCitationCOMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.40, no.6, pp 808 - 817-
dc.citation.titleCOMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION-
dc.citation.volume40-
dc.citation.number6-
dc.citation.startPage808-
dc.citation.endPage817-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.subject.keywordPlusMARGINAL REGRESSION-MODELS-
dc.subject.keywordPlusSURVIVAL-
dc.subject.keywordAuthorEM algorithm-
dc.subject.keywordAuthorInformative cluster size-
dc.subject.keywordAuthorJoint model-
dc.subject.keywordAuthorRandom effects-
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