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Article: Semiparametric Analysis of Clustered Interval-Censored Survival Data with a Cure Fraction

TitleSemiparametric Analysis of Clustered Interval-Censored Survival Data with a Cure Fraction
Authors
Issue Date2014
PublisherElsevier B.V.. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics & Data Analysis, 2014, v. 79, p. 165-174 How to Cite?
AbstractA generalization of the semiparametric Cox's proportional hazards model by means of a random effect or frailty approach to accommodate clustered survival data with a cure fraction is considered. The frailty serves as a quantification of the health condition of the subjects under study and may depend on some observed covariates like age. One single individual-specific frailty that acts on the hazard function is adopted to determine the cure status of an individual and the heterogeneity on the time to event if the individual is not cured. Under this formulation, an individual who has a high propensity to be cured would tend to have a longer time to event if he is not cured. Within a cluster, both the cure statuses and the times to event of the individuals would be correlated. In contrast to some models proposed in the literature, the model accommodates the correlations among observations in a more natural way. A multiple imputation estimation method is proposed for both right-censored and interval-censored data. Simulation studies show that the performance of the proposed estimation method is highly satisfactory. The proposed model and method are applied to the National Aeronautics and Space Administration's hypobaric decompression sickness data to investigate the factors associated with the occurrence and the time to onset of grade IV venous gas emboli under hypobaric environments.
Persistent Identifierhttp://hdl.handle.net/10722/203417

 

DC FieldValueLanguage
dc.contributor.authorLam, KFen_US
dc.contributor.authorWong, KYen_US
dc.date.accessioned2014-09-19T15:10:24Z-
dc.date.available2014-09-19T15:10:24Z-
dc.date.issued2014en_US
dc.identifier.citationComputational Statistics & Data Analysis, 2014, v. 79, p. 165-174en_US
dc.identifier.urihttp://hdl.handle.net/10722/203417-
dc.description.abstractA generalization of the semiparametric Cox's proportional hazards model by means of a random effect or frailty approach to accommodate clustered survival data with a cure fraction is considered. The frailty serves as a quantification of the health condition of the subjects under study and may depend on some observed covariates like age. One single individual-specific frailty that acts on the hazard function is adopted to determine the cure status of an individual and the heterogeneity on the time to event if the individual is not cured. Under this formulation, an individual who has a high propensity to be cured would tend to have a longer time to event if he is not cured. Within a cluster, both the cure statuses and the times to event of the individuals would be correlated. In contrast to some models proposed in the literature, the model accommodates the correlations among observations in a more natural way. A multiple imputation estimation method is proposed for both right-censored and interval-censored data. Simulation studies show that the performance of the proposed estimation method is highly satisfactory. The proposed model and method are applied to the National Aeronautics and Space Administration's hypobaric decompression sickness data to investigate the factors associated with the occurrence and the time to onset of grade IV venous gas emboli under hypobaric environments.en_US
dc.languageengen_US
dc.publisherElsevier B.V.. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_US
dc.relation.ispartofComputational Statistics & Data Analysisen_US
dc.titleSemiparametric Analysis of Clustered Interval-Censored Survival Data with a Cure Fractionen_US
dc.typeArticleen_US
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_US
dc.identifier.authorityLam, KF=rp00718en_US
dc.identifier.doi10.1016/j.csda.2014.05.019-
dc.identifier.hkuros235535en_US
dc.identifier.volume79en_US
dc.identifier.spage165en_US
dc.identifier.epage174en_US

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