Comparison of missing data methods in clustered survival data using Bayesian adaptive B-Spline estimation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoo, Hanna | - |
dc.contributor.author | Lee, Jae Won | - |
dc.date.accessioned | 2021-09-02T13:56:59Z | - |
dc.date.available | 2021-09-02T13:56:59Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-03 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/76798 | - |
dc.description.abstract | In many epidemiological studies, missing values in the outcome arise due to censoring. Such censoring is what makes survival analysis special and differentiated from other analytical methods. There are many methods that deal with censored data in survival analysis. However, few studies have dealt with missing covariates in survival data. Furthermore, studies dealing with missing covariates are rare when data are clustered. In this paper, we conducted a simulation study to compare results of several missing data methods when data had clustered multi-structured type with missing covariates. In this study, we modeled unknown baseline hazard and frailty with Bayesian B-Spline to obtain more smooth and accurate estimates. We also used prior information to achieve more accurate results. We assumed the missing mechanism as MAR. We compared the performance of five different missing data techniques and compared these results through simulation studies. We also presented results from a Multi-Center study of Korean IBD patients with Crohn's disease (Lee et al., Journal of the Korean Society of Coloproctology, 28, 188-194, 2012). | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | KOREAN STATISTICAL SOC | - |
dc.subject | MULTIPLE IMPUTATION | - |
dc.subject | REGRESSION | - |
dc.subject | FRAILTY | - |
dc.subject | MODEL | - |
dc.title | Comparison of missing data methods in clustered survival data using Bayesian adaptive B-Spline estimation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Jae Won | - |
dc.identifier.doi | 10.29220/CSAM.2018.25.2.159 | - |
dc.identifier.scopusid | 2-s2.0-85046899008 | - |
dc.identifier.wosid | 000435616500003 | - |
dc.identifier.bibliographicCitation | COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.25, no.2, pp.159 - 172 | - |
dc.relation.isPartOf | COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS | - |
dc.citation.title | COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS | - |
dc.citation.volume | 25 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 159 | - |
dc.citation.endPage | 172 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002332149 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | MULTIPLE IMPUTATION | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | FRAILTY | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Bayesian adaptive B-spline | - |
dc.subject.keywordAuthor | clustered data | - |
dc.subject.keywordAuthor | MICE | - |
dc.subject.keywordAuthor | missing covariates | - |
dc.subject.keywordAuthor | multiple imputation | - |
dc.subject.keywordAuthor | single imputation | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.