Ph.D. (Doctor of Philosophy)
Department of Mathematical Sciences
Bivariate survival cure rate models extend the understanding of time-to-event data by allowing for the formulation of more accurate and informative conclusion. These conclusions are obtainable from an analysis that accounts for a cured fraction of the population and dependence between paired units. We propose a mixture cure rate model where a correlation coefficient is used for the association between bivariate cure rate fractions and a new generalized Farlie Gumbel Morgenstern (FGM) copula function is applied to model the de-
pendence structure of bivariate survival times. Covariate effects are incorporated into two components of our model, cure rate fractions and marginal distributions. The extremely flexible distributions are employed to model the survival time. The correlation range is improved by the new generalized FGM copula which covers the correlation in the real dataset. We perform goodness-of-fit test for the new copula and illustrate the performance of the proposed method in simulated data and real data via Bayesian paradigm.
Huang, Jie, "Bivariate Cure Rate Model Using Copula Functions in Presence of Censored Data and Covariates" (2019). Graduate Research Theses & Dissertations. 7209.
Northern Illinois University
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