Proportional Hazards Modelling for Current Lifetime Data

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Publicado en:ProQuest Dissertations and Theses (2025)
Autor principal: Sembukuttige, Himansi Vihanga Kumari
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ProQuest Dissertations & Theses
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Acceso en línea:Citation/Abstract
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100 1 |a Sembukuttige, Himansi Vihanga Kumari 
245 1 |a Proportional Hazards Modelling for Current Lifetime Data 
260 |b ProQuest Dissertations & Theses  |c 2025 
513 |a Dissertation/Thesis 
520 3 |a Current lifetime data represent a unique subset of length-biased data where only the times from the initiating event to the cross-section date are observed without follow-up. We propose an efficient estimation procedure to fit the semi-parametric proportional hazards model using current lifetime data. This estimation procedure is based on the EM algorithm which has two versions based on the support points of the non-parametric baseline hazard function. We apply the method to simulated data and Parkinson’s disease current lifetime data drawn from the Canadian-Open Parkinson Network (C-OPN). We estimate the effects of clinical and epidemiological covariates using the proportional hazards model on the survival of subjects with Parkinson’s disease. 
653 |a Lifetime 
653 |a Failure 
653 |a Normal distribution 
653 |a Cohort analysis 
653 |a Binomial distribution 
653 |a Probability 
653 |a Survival analysis 
653 |a Parkinson's disease 
653 |a Parameter estimation 
653 |a Neurosciences 
773 0 |t ProQuest Dissertations and Theses  |g (2025) 
786 0 |d ProQuest  |t ProQuest Dissertations & Theses Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3283378744/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3283378744/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u https://hdl.handle.net/10294/16851