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Regression modelling of overall survival and progression-free survival

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dc.contributor.advisor Darlington, Gerarda
dc.contributor.author Chen, Yi
dc.date.accessioned 2020-01-09T22:27:49Z
dc.date.available 2020-01-09T22:27:49Z
dc.date.copyright 2020-01
dc.date.created 2019-12-16
dc.date.issued 2020-01-09
dc.identifier.uri http://hdl.handle.net/10214/17764
dc.description.abstract There are three endpoints commonly used in oncology clinical trials, which are known as overall survival (OS), time to progression (TTP) and progression-free survival (PFS). Recently, PFS has become an important alternative endpoint to OS. In this thesis, both exponential and Weibull distributions are used to investigate the joint model of OS and PFS. Regression modelling will be introduced to investigate the effect of a treatment indicator on the distribution parameters for OS, TTP, and PFS. Both simulated data and real data will be used to investigate and demonstrate methods. The parameters of the models will be estimated by the maximum likelihood estimation. Furthermore, Wald tests will be performed to investigate covariate effects. en_US
dc.language.iso en en_US
dc.subject regression model en_US
dc.subject overall survival en_US
dc.subject progression-free survival en_US
dc.subject joint model en_US
dc.title Regression modelling of overall survival and progression-free survival en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Mathematics and Statistics en_US
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