Bayesian Dose-Finding Procedure Based on Information Criterion and Efficacy-Toxicity Trade-offs

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dc.contributor.advisorRosenberger, William F.
dc.contributor.authorGao, Lei
dc.creatorGao, Lei
dc.date.accessioned2014-09-18T01:56:57Z
dc.date.available2014-09-18T01:56:57Z
dc.date.issued2014-05
dc.description.abstractIn dose-finding studies with toxicity-efficacy responses, utility functions and Bayesian procedures are used to find a single optimal dose with ethical toxicity-efficacy trade-offs. We demonstrate that the design can have convergence issues when the prior information is misspecified. We propose to incorporate an information penalty to obtain multiple-dose allocation with efficient ethical measures. A coefficient is introduced to control the trade-off between information gain and ethical gain. We conduct simulations using Markov chain Monte Carlo (MCMC) algorithms to examine the convergence of Bayesian dose finding designs and investigate their operating characteristics. Different stopping rules are considered and their benefits are demonstrated by simulation. Guidance on how to select design parameters are given in two hypothetical trial redesigns.
dc.format.extent163 pages
dc.identifier.urihttps://hdl.handle.net/1920/8915
dc.language.isoen
dc.rightsCopyright 2014 Lei Gao
dc.subjectStatistics
dc.subjectBayesian
dc.subjectDose-Finding
dc.subjectEfficacy-Toxicity Trade-offs
dc.subjectFisher information matrix
dc.subjectOptimal Design
dc.subjectSequential design
dc.titleBayesian Dose-Finding Procedure Based on Information Criterion and Efficacy-Toxicity Trade-offs
dc.typeDissertation
thesis.degree.disciplineStatistical Science
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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