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

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dc.contributor.advisor Rosenberger, William F.
dc.contributor.author Gao, Lei
dc.creator Gao, Lei en_US
dc.date.accessioned 2014-09-18T01:56:57Z
dc.date.available 2014-09-18T01:56:57Z
dc.date.issued 2014-05 en_US
dc.identifier.uri https://hdl.handle.net/1920/8915
dc.description.abstract In 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.extent 163 pages en_US
dc.language.iso en en_US
dc.rights Copyright 2014 Lei Gao en_US
dc.subject Statistics en_US
dc.subject Bayesian en_US
dc.subject Dose-Finding en_US
dc.subject Efficacy-Toxicity Trade-offs en_US
dc.subject Fisher information matrix en_US
dc.subject Optimal Design en_US
dc.subject Sequential design en_US
dc.title Bayesian Dose-Finding Procedure Based on Information Criterion and Efficacy-Toxicity Trade-offs en_US
dc.type Dissertation en
thesis.degree.level Doctoral en
thesis.degree.discipline Statistical Science en
thesis.degree.grantor George Mason University en


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