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Article: Multilevel modelling of clustered grouped survival data using Cox regression model: An application to ART dental restorations

TitleMultilevel modelling of clustered grouped survival data using Cox regression model: An application to ART dental restorations
Authors
KeywordsBayesian approach
Clustered grouped survival data
Cox regression
Multilevel modelling
Issue Date2006
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics In Medicine, 2006, v. 25 n. 3, p. 447-457 How to Cite?
AbstractIn some controlled clinical trials in dental research, multiple failure time data from the same patient are frequently observed that result in clustered multiple failure time. Moreover, the treatments are often delivered by more than one operator and thus the multiple failure times are clustered according to a multilevel structure when the operator effects are assumed to be random. In practice, it is often too expensive or even impossible to monitor the study subjects continuously, but they are examined periodically at some regular pre-scheduled visits. Hence, discrete or grouped clustered failure time data are collected. The aim of this paper is to illustrate the use of the Monte Carlo Markov chain (MCMC) approach and non-informative prior in a Bayesian framework to mimic the maximum likelihood (ML) estimation in a frequentist approach in multilevel modelling of clustered grouped survival data. A three-level model with additive variance components model for the random effects is considered in this paper. Both the grouped proportional hazards model and the dynamic logistic regression model are used. The approximate intra-cluster correlation of the log failure times can be estimated when the grouped proportional hazards model is used. The statistical package WinBUGS is adopted to estimate the parameter of interest based on the MCMC method. The models and method are applied to a data set obtained from a prospective clinical study on a cohort of Chinese school children that atraumatic restorative treatment (ART) restorations were placed on permanent teeth with carious lesions. Altogether 284 ART restorations were placed by five dentists and clinical status of the ART restorations was evaluated annually for 6 years after placement, thus clustered grouped failure times of the restorations were recorded. Results based on the grouped proportional hazards model revealed that clustering effect among the log failure times of the different restorations from the same child was fairly strong (corrchild = 0.55) but the effects attributed to the dentists could be regarded as negligible (corrdentist = 0.03). Gender and the location of the restoration were found to have no effects on the failure times and no difference in failure times was found between small restorations placed on molars and non-molars. Large restorations placed on molars were found to have shorter failure times compared to small restorations. The estimates of the baseline parameters were increasing indicating increasing hazard rates from interval 1 to 6. Results based on the logistic regression models were similar. In conclusion, the use of the MCMC approach and non-informative prior in a Bayesian framework to mimic the ML estimation in a frequentist approach in multilevel modelling of clustered grouped survival data can be easily applied with the use of the software WinBUGS. Copyright © 2005 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/66072
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWong, MCMen_HK
dc.contributor.authorLam, KFen_HK
dc.contributor.authorLo, ECMen_HK
dc.date.accessioned2010-09-06T05:43:21Z-
dc.date.available2010-09-06T05:43:21Z-
dc.date.issued2006en_HK
dc.identifier.citationStatistics In Medicine, 2006, v. 25 n. 3, p. 447-457en_HK
dc.identifier.issn0277-6715en_HK
dc.identifier.urihttp://hdl.handle.net/10722/66072-
dc.description.abstractIn some controlled clinical trials in dental research, multiple failure time data from the same patient are frequently observed that result in clustered multiple failure time. Moreover, the treatments are often delivered by more than one operator and thus the multiple failure times are clustered according to a multilevel structure when the operator effects are assumed to be random. In practice, it is often too expensive or even impossible to monitor the study subjects continuously, but they are examined periodically at some regular pre-scheduled visits. Hence, discrete or grouped clustered failure time data are collected. The aim of this paper is to illustrate the use of the Monte Carlo Markov chain (MCMC) approach and non-informative prior in a Bayesian framework to mimic the maximum likelihood (ML) estimation in a frequentist approach in multilevel modelling of clustered grouped survival data. A three-level model with additive variance components model for the random effects is considered in this paper. Both the grouped proportional hazards model and the dynamic logistic regression model are used. The approximate intra-cluster correlation of the log failure times can be estimated when the grouped proportional hazards model is used. The statistical package WinBUGS is adopted to estimate the parameter of interest based on the MCMC method. The models and method are applied to a data set obtained from a prospective clinical study on a cohort of Chinese school children that atraumatic restorative treatment (ART) restorations were placed on permanent teeth with carious lesions. Altogether 284 ART restorations were placed by five dentists and clinical status of the ART restorations was evaluated annually for 6 years after placement, thus clustered grouped failure times of the restorations were recorded. Results based on the grouped proportional hazards model revealed that clustering effect among the log failure times of the different restorations from the same child was fairly strong (corrchild = 0.55) but the effects attributed to the dentists could be regarded as negligible (corrdentist = 0.03). Gender and the location of the restoration were found to have no effects on the failure times and no difference in failure times was found between small restorations placed on molars and non-molars. Large restorations placed on molars were found to have shorter failure times compared to small restorations. The estimates of the baseline parameters were increasing indicating increasing hazard rates from interval 1 to 6. Results based on the logistic regression models were similar. In conclusion, the use of the MCMC approach and non-informative prior in a Bayesian framework to mimic the ML estimation in a frequentist approach in multilevel modelling of clustered grouped survival data can be easily applied with the use of the software WinBUGS. Copyright © 2005 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.subjectBayesian approachen_HK
dc.subjectClustered grouped survival dataen_HK
dc.subjectCox regressionen_HK
dc.subjectMultilevel modellingen_HK
dc.subject.meshAdolescenten_HK
dc.subject.meshBayes Theoremen_HK
dc.subject.meshChilden_HK
dc.subject.meshClinical Trials as Topic - methodsen_HK
dc.subject.meshCluster Analysisen_HK
dc.subject.meshCohort Studiesen_HK
dc.subject.meshDental Restoration Failureen_HK
dc.subject.meshDental Restoration, Permanenten_HK
dc.subject.meshFemaleen_HK
dc.subject.meshHumansen_HK
dc.subject.meshMaleen_HK
dc.subject.meshMarkov Chainsen_HK
dc.subject.meshMonte Carlo Methoden_HK
dc.subject.meshProportional Hazards Modelsen_HK
dc.titleMultilevel modelling of clustered grouped survival data using Cox regression model: An application to ART dental restorationsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0277-6715&volume=25&spage=447&epage=457&date=2006&atitle=Multilevel+modelling+of+clustered+grouped+survival+data+using+Cox+regression+model:+an+application+to+ART+dental+restorationsen_HK
dc.identifier.emailWong, MCM: mcmwong@hkucc.hku.hken_HK
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_HK
dc.identifier.emailLo, ECM: hrdplcm@hkucc.hku.hken_HK
dc.identifier.authorityWong, MCM=rp00024en_HK
dc.identifier.authorityLam, KF=rp00718en_HK
dc.identifier.authorityLo, ECM=rp00015en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/sim.2235en_HK
dc.identifier.pmid16143989en_HK
dc.identifier.scopuseid_2-s2.0-31744448992en_HK
dc.identifier.hkuros114528en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-31744448992&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume25en_HK
dc.identifier.issue3en_HK
dc.identifier.spage447en_HK
dc.identifier.epage457en_HK
dc.identifier.isiWOS:000235092100006-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWong, MCM=26029250900en_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridLo, ECM=7101705982en_HK

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