Publication Date

2019

Document Type

Dissertation/Thesis

First Advisor

Ebrahimi, Nader

Degree Name

Ph.D. (Doctor of Philosophy)

Legacy Department

Department of Mathematical Sciences

Abstract

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.

Extent

117 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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