QA452 : Bayesian analysis of non-mixture cure rate models with a Cox semi-parametric survival function by using P-splines
Thesis > Central Library of Shahrood University > Mathematical Sciences > MSc > 2018
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Abstarct:
In the analysis of survival data, it is usually assumed that any unit will experience the
event of interest if it is observed for a sufficiently long time. However, it can be assumed
that an unknown proportion of the population under study will never experience the
interest event.The Promotion time model, which has a biological motivation, is one of the
survival models taking this feature in to account. The survival function of uncured people
is estimated by using a Cox proportional hazard model where the logarithm of the baxseline
hazard function is specified using Bayesian P-splines. We use a Bayesian frxamework for
implementing Statistical inference in the proposed model. The identification issues of the
model are discussed and a restricted use of the model when the follow-up of the study is
not sufficiently long is suggested. The accuracy of our methodology is assumed through a
simulation study and the model is illustrated on a dataset from a Melanoma clinical trial.
Keywords:
#Survival analysis #Promotion time model #P-spline #Cox proportional hazard model #Bayesian inference Link
Keeping place: Central Library of Shahrood University
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Keeping place: Central Library of Shahrood University
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