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dc.contributor.authorNiu, Yien
dc.date2013-01-30 21:23:48.968
dc.date.accessioned2013-02-01T17:04:58Z
dc.date.available2013-02-01T17:04:58Z
dc.date.issued2013-02-01
dc.identifier.urihttp://hdl.handle.net/1974/7796
dc.descriptionThesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2013-01-30 21:23:48.968en
dc.description.abstractClustered failure time data often arise in biomedical and clinical studies where potential correlation among survival times is induced in a cluster. In this thesis, we develop a class of marginal models for right censored clustered failure time data and propose a novel generalized estimating equation approach in a likelihood-based context. We first investigate a semiparametric proportional hazards model for clustered survival data and derive the large sample properties of the regression estimators. The finite sample studies demonstrate that the good applicability of the proposed method as well as the substantial efficiency improvement in comparison with the existing marginal model for clustered survival data. Another important feature of failure time data we will consider in this thesis is a possible fraction of cured subjects. To accommodate the potential cure fraction, we consider a proportional hazards mixture cure model for clustered survival data with long-term survivors and develop a set of estimating equations by incorporating working correlation matrices in an EM algorithm. The dependence among the cure statuses and among the survival times of uncured patients within clusters are modeled by working correlation matrices in the estimating equations. For the parametric proportional hazards mixture cure model, we show that the estimators of the regression parameters and the parameter in the baseline hazard function are consistent and asymptotically normal with a sandwich covariance matrix that can be consistently estimated. A numerical study presents that the proposed estimation method is comparable with the existing parametric marginal method. We also extend the proposed generalized estimating equation approach to a semiparametric proportional hazards mixture cure model where the baseline survival function is nonparametrically specified. A bootstrap method is used to obtain the variances of the estimates. The proposed method is evaluated by a simulation study from which we observe a noticeable efficiency gain of the proposed method over the existing semiparametric marginal method for clustered failure time data with a cure fraction.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectSurvival Analysisen
dc.subjectMultivariate Analysisen
dc.subjectBiostatisticsen
dc.subjectPublic Healthen
dc.subjectCancer Researchen
dc.subjectStatisticsen
dc.titleMarginal Models for Modeling Clustered Failure Time Dataen
dc.typethesisen
dc.description.degreePhDen
dc.contributor.supervisorPeng, Paul Yingweien
dc.contributor.departmentMathematics and Statisticsen
dc.degree.grantorQueen's University at Kingstonen


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