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Article: A multiple imputation approach for clustered interval-censored survival data

TitleA multiple imputation approach for clustered interval-censored survival data
Authors
Issue Date2010
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics In Medicine, 2010, v. 29 n. 6, p. 680-693 How to Cite?
AbstractMultivariate interval-censored failure time data arise commonly in many studies of epidemiology and biomedicine. Analysis of these type of data is more challenging than the right-censored data. We propose a simple multiple imputation strategy to recover the order of occurrences based on the interval-censored event times using a conditional predictive distribution function derived from a parametric gamma random effects model. By imputing the interval-censored failure times, the estimation of the regression and dependence parameters in the context of a gamma frailty proportional hazards model using the well-developed EM algorithm is made possible. A robust estimator for the covariance matrix is suggested to adjust for the possible misspecification of the parametric baseline hazard function. The finite sample properties of the proposed method are investigated via simulation. The performance of the proposed method is highly satisfactory, whereas the computation burden is minimal. The proposed method is also applied to the diabetic retinopathy study (DRS) data for illustration purpose and the estimates are compared with those based on other existing methods for bivariate grouped survival data. Copyright © 2010 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/172469
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KFen_US
dc.contributor.authorXu, Yen_US
dc.contributor.authorCheung, TLen_US
dc.date.accessioned2012-10-30T06:22:41Z-
dc.date.available2012-10-30T06:22:41Z-
dc.date.issued2010en_US
dc.identifier.citationStatistics In Medicine, 2010, v. 29 n. 6, p. 680-693en_US
dc.identifier.issn0277-6715en_US
dc.identifier.urihttp://hdl.handle.net/10722/172469-
dc.description.abstractMultivariate interval-censored failure time data arise commonly in many studies of epidemiology and biomedicine. Analysis of these type of data is more challenging than the right-censored data. We propose a simple multiple imputation strategy to recover the order of occurrences based on the interval-censored event times using a conditional predictive distribution function derived from a parametric gamma random effects model. By imputing the interval-censored failure times, the estimation of the regression and dependence parameters in the context of a gamma frailty proportional hazards model using the well-developed EM algorithm is made possible. A robust estimator for the covariance matrix is suggested to adjust for the possible misspecification of the parametric baseline hazard function. The finite sample properties of the proposed method are investigated via simulation. The performance of the proposed method is highly satisfactory, whereas the computation burden is minimal. The proposed method is also applied to the diabetic retinopathy study (DRS) data for illustration purpose and the estimates are compared with those based on other existing methods for bivariate grouped survival data. Copyright © 2010 John Wiley & Sons, Ltd.en_US
dc.languageengen_US
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/en_US
dc.relation.ispartofStatistics in Medicineen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshBiomedical Research - Statistics & Numerical Dataen_US
dc.subject.meshCluster Analysisen_US
dc.subject.meshDiabetic Retinopathyen_US
dc.subject.meshHumansen_US
dc.subject.meshSurvival Analysisen_US
dc.titleA multiple imputation approach for clustered interval-censored survival dataen_US
dc.typeArticleen_US
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_US
dc.identifier.authorityLam, KF=rp00718en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1002/sim.3835en_US
dc.identifier.pmid20069624-
dc.identifier.scopuseid_2-s2.0-77649197304en_US
dc.identifier.hkuros170550-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77649197304&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume29en_US
dc.identifier.issue6en_US
dc.identifier.spage680en_US
dc.identifier.epage693en_US
dc.identifier.eissn1097-0258-
dc.identifier.isiWOS:000275691200007-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridLam, KF=8948421200en_US
dc.identifier.scopusauthoridXu, Y=36106595800en_US
dc.identifier.scopusauthoridCheung, TL=36105681600en_US
dc.identifier.citeulike6707439-

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