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• W1444088 abstract "Analyses involving longitudinal and time-to-event data are quite common inmedical research. The primary goal of such studies to simultaneously study theeffect of treatment on both the longitudinal covariate and survival. Often in medicalresearch, there are settings in which it is meaningful to consider the existence of afraction of individuals who have little to no risk of experiencing the event ofinterest. In this thesis, we focus on such settings with two different data structures.In early part of the thesis, we focus on the use of a cured fraction survival modelsperformed in a population-based cancer registries. The limitations of statisticalmodels which embodied the concept of a cured fraction of patients lack flexibilityfor modelling the survival distribution of the uncured group; lead to a not good fitwhen the survival drops rapidly soon after diagnosis and also when the survival istoo high. In this study, a cure mixture model is enhanced by developing a dynamicsemi-parametric exponential function with a smoothing parameter. The latter (major) part of the thesis focuses on modelling the longitudinal and thesurvival data in presence of cure fraction jointly. When there are cured patients inthe population, the existing methods of joint models would be inappropriate, sincethey do not account for the plateau in the survival function. We introduce a newclass of joint models in presence of cure fraction. In this joint model, thelongitudinal submodel is a combination of a random mixed effect model and astochastic process. A semi-parametric submodel is also proposed to incorporate thetrue longitudinal trajectories and other baseline time (dependent or independent)covariates. This model accounts for the possibility that a subject is cured, for theunique nature of the longitudinal data, and is capable to accommodating both zeroand nonzero cure fractions. We generalize the two submodels to bemultidimensional to investigate the relationship between the multivariatelongitudinal and survival data.Bayesian approach was applied to the data using a conjugate and non-conjugateprior families to obtain parameter estimates for the proposed models. Gibbssampling scheme is modified for fitting the joint model. Metropolis Hasting andAdaptive Rejection Sampling steps are used to update the Markov chain to estimateparameter whose full conditional densities can not be sampled efficiently from theexisting methods, leading us to propose efficient proposal densities.The simulation studies demonstrate that the joint modelling method results inefficient estimates and good coverage for the population parameters. The analysis ofcancer patient’s data indicates that when ignoring the association between thelongitudinal and the survival data would lead to biased estimates for the mostimportant parameters." @default.
• W1444088 created "2016-06-24" @default.
• W1444088 creator A5068208037 @default.
• W1444088 date "2008-01-01" @default.
• W1444088 modified "2023-09-27" @default.
• W1444088 title "Joint Modelling Of Longitudinal and Survival Data in Presence of Cure Fraction with Application to Cancer Patients’ Data" @default.
• W1444088 hasPublicationYear "2008" @default.
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