Efficient Estimation of Partly Linear Transformation Model with Interval-censored Competing Risks Data

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dc.contributor.advisorLu, Xuewen
dc.contributor.authorWang, Yan
dc.contributor.committeememberShen, Hua
dc.contributor.committeememberChekouo, Thierry T.
dc.date2019-11
dc.date.accessioned2019-09-25T17:30:00Z
dc.date.available2019-09-25T17:30:00Z
dc.date.issued2019-09-19
dc.description.abstractWe consider the class of semiparametric generalized odds rate transformation models to estimate the cause-specific cumulative incidence function, which is an important quantity under competing risks framework, and assess the contribution of covariates with interval-censored competing risks data. The model is able to handle both linear and non-linear components. The baseline cumulative incidence functions and non-linear components of different competing risks are approximated with B-spline basis functions or Bernstein polynomials, and the estimated parameters are obtained by employing the sieve maximum likelihood estimation. We designed two examples in the simulation studies and the simulation results show that the method performs well. We used the proposed method to analyze the HIV data obtained from patients in a large cohort study in sub-Saharan Africa.en_US
dc.identifier.citationWang, Y. (2019). Efficient Estimation of Partly Linear Transformation Model with Interval-censored Competing Risks Data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/37128
dc.identifier.urihttp://hdl.handle.net/1880/111066
dc.language.isoengen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectsemi-parametric model, interval-censored data, competing risks, estimation.en_US
dc.subject.classificationStatisticsen_US
dc.titleEfficient Estimation of Partly Linear Transformation Model with Interval-censored Competing Risks Dataen_US
dc.typemaster thesisen_US
thesis.degree.disciplineMathematics & Statisticsen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopyfalseen_US
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