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Article: REML and ML estimation for clustered grouped survival data

TitleREML and ML estimation for clustered grouped survival data
Authors
KeywordsClustered grouped survival data
Intracluster correlation
Residual maximum likelihood
Shared random effect
Issue Date2003
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics In Medicine, 2003, v. 22 n. 12, p. 2025-2034 How to Cite?
AbstractClustered grouped survival data arise naturally in clinical medicine and biological research. For example, in a randomized clinical trial, the variable of interest is the time to occurrence of a certain event with or without a new treatment and the data are collected from possibly correlated subjects from independent clusters. However it is sometimes impossible or too expensive to monitor the experimental subjects continuously. The subjects are examined regularly and the continuous survival data are thus grouped into a discrete time scale. With such a design, researchers are mainly interested in the effectiveness of the new treatment as well as the correlation among subjects from the same cluster, namely the intracluster correlation. This paper suggests a random effects approach to the estimation of the regression parameter with various choices of regression model and also the dependence parameter which characterizes the intracluster correlation. Time dependent covariates can be accommodated in the proposed model, and the estimation procedure will not be further complicated with large cluster sizes. The proposed method is applied to the data from the Diabetic Retinopathy Study, the objective of which is to evaluate the effectiveness of laser photocoagulation in delaying or preventing the onset of blindness in the left and right eyes of individuals with diabetes-associated retinopathy. The intracluster correlation using a grouped proportional hazards regression model can be estimated and the relationship between the regression parameter estimates based on the random effects approach and the marginal approach using a dynamic logistic regression model are discussed. Results from a simulation study of the proposed method are also presented. Copyright © 2003 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/82652
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KFen_HK
dc.contributor.authorIp, Den_HK
dc.date.accessioned2010-09-06T08:31:51Z-
dc.date.available2010-09-06T08:31:51Z-
dc.date.issued2003en_HK
dc.identifier.citationStatistics In Medicine, 2003, v. 22 n. 12, p. 2025-2034en_HK
dc.identifier.issn0277-6715en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82652-
dc.description.abstractClustered grouped survival data arise naturally in clinical medicine and biological research. For example, in a randomized clinical trial, the variable of interest is the time to occurrence of a certain event with or without a new treatment and the data are collected from possibly correlated subjects from independent clusters. However it is sometimes impossible or too expensive to monitor the experimental subjects continuously. The subjects are examined regularly and the continuous survival data are thus grouped into a discrete time scale. With such a design, researchers are mainly interested in the effectiveness of the new treatment as well as the correlation among subjects from the same cluster, namely the intracluster correlation. This paper suggests a random effects approach to the estimation of the regression parameter with various choices of regression model and also the dependence parameter which characterizes the intracluster correlation. Time dependent covariates can be accommodated in the proposed model, and the estimation procedure will not be further complicated with large cluster sizes. The proposed method is applied to the data from the Diabetic Retinopathy Study, the objective of which is to evaluate the effectiveness of laser photocoagulation in delaying or preventing the onset of blindness in the left and right eyes of individuals with diabetes-associated retinopathy. The intracluster correlation using a grouped proportional hazards regression model can be estimated and the relationship between the regression parameter estimates based on the random effects approach and the marginal approach using a dynamic logistic regression model are discussed. Results from a simulation study of the proposed method are also presented. Copyright © 2003 John Wiley & Sons, Ltd.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/en_HK
dc.relation.ispartofStatistics in Medicineen_HK
dc.rightsStatistics in Medicine. Copyright © John Wiley & Sons Ltd.en_HK
dc.subjectClustered grouped survival dataen_HK
dc.subjectIntracluster correlationen_HK
dc.subjectResidual maximum likelihooden_HK
dc.subjectShared random effecten_HK
dc.titleREML and ML estimation for clustered grouped survival dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0277-6715&volume=22&spage=2025&epage=2034&date=2003&atitle=REML+and+ML+estimation+for+clustered+grouped+survival+dataen_HK
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_HK
dc.identifier.authorityLam, KF=rp00718en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/sim.1323en_HK
dc.identifier.pmid12802820-
dc.identifier.scopuseid_2-s2.0-0037529418en_HK
dc.identifier.hkuros76662en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0037529418&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume22en_HK
dc.identifier.issue12en_HK
dc.identifier.spage2025en_HK
dc.identifier.epage2034en_HK
dc.identifier.isiWOS:000183610200007-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridIp, D=36913434300en_HK

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