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Survival analysis: Part I - analysis of time-to-event

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dc.contributor.authorIn, Junyong-
dc.contributor.authorLee, Dong Kyu-
dc.date.accessioned2023-04-27T08:40:21Z-
dc.date.available2023-04-27T08:40:21Z-
dc.date.issued2018-06-
dc.identifier.issn2005-6419-
dc.identifier.issn2005-7563-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/2077-
dc.description.abstractLength of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death. This review introduces methods of analyzing time-to-event. The Kaplan-Meier survival analysis, log-rank test, and Cox proportional hazards regression modeling method are described with examples of hypothetical data.-
dc.format.extent10-
dc.language영어-
dc.language.isoENG-
dc.publisher대한마취통증의학회-
dc.titleSurvival analysis: Part I - analysis of time-to-event-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.4097/kja.d.18.00067-
dc.identifier.scopusid2-s2.0-85049589503-
dc.identifier.bibliographicCitationKorean Journal of Anesthesiology, v.71, no.3, pp 182 - 191-
dc.citation.titleKorean Journal of Anesthesiology-
dc.citation.volume71-
dc.citation.number3-
dc.citation.startPage182-
dc.citation.endPage191-
dc.type.docTypeCorrection-
dc.identifier.kciidART002350773-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorCensored data-
dc.subject.keywordAuthorCox regression-
dc.subject.keywordAuthorHazard ratio-
dc.subject.keywordAuthorKaplan-Meier method-
dc.subject.keywordAuthorLog-rank test-
dc.subject.keywordAuthorMedical statistics-
dc.subject.keywordAuthorPower analysis-
dc.subject.keywordAuthorProportional hazards-
dc.subject.keywordAuthorSample size-
dc.subject.keywordAuthorSurvival analysis-
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