Bayesian analysis and applications of a model for survival data with a surviving fraction

Tong, Qian (2002) Bayesian analysis and applications of a model for survival data with a surviving fraction. Masters thesis, Memorial University of Newfoundland.

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    Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
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Abstract

Cure rate estimation is one of the most important issues in clinical trials and cure rate models are the main models. In the past decade, the standard cure rate model has been discussed and used. However, this model involves several drawbacks. Chen, Ibrahim and Sinha (1999) considered Bayesian methods for right-censored survival data for populations with a surviving (cure) fraction. In that paper, the authors proposed the cure rate model under the Weibull distribution which is quite different from the standard cure rate model. This proposed cure rate model overcomes the drawbacks of the standard cure rate model. However, it is not clear from their work whether their proposed cure rate models can be extended to other distributions. In this practicum, we shall extend those proposed cure rate models in Chen et al (1999) to the following distributions: log-logistic, Gompertz, and Gamma. Prior elicitations will also be discussed in detail, and classes of noninformative and informative prior distributions will be proposed. Furthermore, several theoretical properties of the proposed priors and resulting posteriors will be derived. -- At the end of this practicum, a melanoma clinical trial is used to illustrate applications of the log-logistic, Gompertz and Gamma distributions to the proposed cure rate models for Bayesian analysis. -- Keywords: Cure rate model; Historical data; Current data; Posterior distribution; Gamma distribution; Log-logistic distribution; Gompertz distribution.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/1127
Item ID: 1127
Additional Information: Bibliography: leaves 79-80.
Department(s): Science, Faculty of > Mathematics and Statistics
Date: 2002
Date Type: Submission
Library of Congress Subject Heading: Bayesian statistical decision theory; Survival analysis (Biometry)

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