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Article: Analysis of multilevel grouped survival data with time-varying regression coefficients

TitleAnalysis of multilevel grouped survival data with time-varying regression coefficients
Authors
KeywordsBayesian approach
Clustered grouped survival data
Cox regression
Multilevel modeling
Time-varying regression coefficients
Issue Date2011
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics In Medicine, 2011, v. 30 n. 3, p. 250-259 How to Cite?
AbstractCorrelated or multilevel grouped survival data are common in medical and dental research. Two common approaches to analyze such data are the marginal and the random-effects approaches. Models and methods in the literature generally assume that the treatment effect is constant over time. A researcher may be interested in studying whether the treatment effects in a clinical trial vary over time, say fade out gradually. This is of particular clinical value when studying the long-term effect of a treatment. This paper proposed to extend the random effects grouped proportional hazards models by incorporating the possibly time-varying covariate effects into the model in terms of a state-space formulation. The proposed model is very flexible and the estimation can be performed using the MCMC approach with non-informative priors in the Bayesian framework. The method is applied to a data set from a prospective clinical trial investigating the effectiveness of silver diamine fluoride (SDF) and sodium fluoride (NaF) varnish in arresting active dentin caries in the Chinese preschool children. It is shown that the treatment groups with caries removal prior to the topical fluoride applications are most effective in shortening the arrest times in the first 6-month interval, but their effects fade out rapidly since then. The effects of treatment groups without caries removal prior to topical fluoride application drop at a very slow rate and can be considered as more or less constant over time. The applications of SDF solution is found to be more effective than the applications of NaF vanish. Copyright © 2010 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/142268
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of the Hong Kong Special Administrative Region, ChinaHKU 7527/05M
Funding Information:

Thanks to Dr C. H. Chu for his kind permission for us to use the data set for this paper. The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU 7527/05M).

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorWong, MCMen_HK
dc.contributor.authorLam, KFen_HK
dc.contributor.authorLo, ECMen_HK
dc.date.accessioned2011-10-28T02:41:57Z-
dc.date.available2011-10-28T02:41:57Z-
dc.date.issued2011en_HK
dc.identifier.citationStatistics In Medicine, 2011, v. 30 n. 3, p. 250-259en_HK
dc.identifier.issn0277-6715en_HK
dc.identifier.urihttp://hdl.handle.net/10722/142268-
dc.description.abstractCorrelated or multilevel grouped survival data are common in medical and dental research. Two common approaches to analyze such data are the marginal and the random-effects approaches. Models and methods in the literature generally assume that the treatment effect is constant over time. A researcher may be interested in studying whether the treatment effects in a clinical trial vary over time, say fade out gradually. This is of particular clinical value when studying the long-term effect of a treatment. This paper proposed to extend the random effects grouped proportional hazards models by incorporating the possibly time-varying covariate effects into the model in terms of a state-space formulation. The proposed model is very flexible and the estimation can be performed using the MCMC approach with non-informative priors in the Bayesian framework. The method is applied to a data set from a prospective clinical trial investigating the effectiveness of silver diamine fluoride (SDF) and sodium fluoride (NaF) varnish in arresting active dentin caries in the Chinese preschool children. It is shown that the treatment groups with caries removal prior to the topical fluoride applications are most effective in shortening the arrest times in the first 6-month interval, but their effects fade out rapidly since then. The effects of treatment groups without caries removal prior to topical fluoride application drop at a very slow rate and can be considered as more or less constant over time. The applications of SDF solution is found to be more effective than the applications of NaF vanish. Copyright © 2010 John Wiley & Sons, Ltd.en_HK
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_HK
dc.relation.ispartofStatistics in Medicineen_HK
dc.rightsStatistics in Medicine. Copyright © John Wiley & Sons Ltd.-
dc.subjectBayesian approachen_HK
dc.subjectClustered grouped survival dataen_HK
dc.subjectCox regressionen_HK
dc.subjectMultilevel modelingen_HK
dc.subjectTime-varying regression coefficientsen_HK
dc.subject.meshControlled Clinical Trials as Topic - methods-
dc.subject.meshDental Caries - prevention and control-
dc.subject.meshProportional Hazards Models-
dc.subject.meshQuaternary Ammonium Compounds - therapeutic use-
dc.subject.meshSurvival Analysis-
dc.titleAnalysis of multilevel grouped survival data with time-varying regression coefficientsen_HK
dc.typeArticleen_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.4094en_HK
dc.identifier.pmid21213342-
dc.identifier.scopuseid_2-s2.0-78650926518en_HK
dc.identifier.hkuros184279en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78650926518&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume30en_HK
dc.identifier.issue3en_HK
dc.identifier.spage250en_HK
dc.identifier.epage259en_HK
dc.identifier.eissn1097-0258-
dc.identifier.isiWOS:000286908500005-
dc.publisher.placeUnited Kingdomen_HK
dc.relation.projectSemiparametric regression analysis for clustered interval censored survival data-
dc.identifier.scopusauthoridWong, MCM=26029250900en_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridLo, ECM=7101705982en_HK
dc.identifier.citeulike8664141-

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