Author Type

Graduate Student

Date of Award

2018

Document Type

Dissertation

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Qian, Lianfen

Abstract

Analyzing life-time data with long-term survivors is an important topic in medical application. Cure models are usually used to analyze survival data with the proportion of cure subjects or long-term survivors. In order to include the propor- tion of cure subjects, mixture and non-mixture cure models are considered. In this dissertation, we utilize both maximum likelihood and Bayesian methods to estimate model parameters. Simulation studies are carried out to verify the nite sample per- formance of the estimation methods. Real data analyses are reported to illustrate the goodness-of- t via Fr echet, Weibull and Exponentiated Exponential susceptible distributions. Among the three parametric susceptible distributions, Fr echet is the most promising. Next, we extend the non-mixture cure model to include a change point in a covariate for right censored data. The smoothed likelihood approach is used to address the problem of a log-likelihood function which is not di erentiable with respect to the change point. The simulation study is based on the non-mixture change point cure model with an exponential distribution for the susceptible subjects. The simulation results revealed a convincing performance of the proposed method of estimation.

Extent

95 p.

Comments

Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2018.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00013083

Format

pdf

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