Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555104
Title: Using prior information in clinical trial design
Author: Ren, Shijie
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2011
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Abstract:
A current concern in medical research is low productivity In the pharmaceutical industry. Failure rates of Phase III clinical trials are high, and this is very costly in terms of using resources and money. Our aim in this thesis is to incorporate prior information in clinical trial design and develop better assessments of the chances of successful clinical trials, so that trial sponsors can improve their success rates. Assurance calculations, which take into account uncertainty about how effective the treatment actually is, provide a more reliable assessment of the probability of a successful trial outcome comparing with power calculations. We develop assurance methods to accommodate survival outcome measures, assuming both parametric and nonparametric models. We also develop prior elicitation procedures for each survival model so that the assurance calculations can be performed more easily and reliably. Prior elicitation is not an easy task, and we may be uncertain about what distribution 'best' represents an expert's beliefs. We demonstrated that robustness of the assurance to different choices of prior distribution can be assessed by treating the elicitation process as a Bayesian inference problem, using a nonparametric Bayesian approach to quantify uncertainty in the expert's density function of the true treatment effect. In this thesis, we also consider a decision-making problem for a single-arm open label Phase 11 trial for the PhD sponsor Roche. Based on the Bayesian decision- theoretic approach and assurance calculations, a model is developed for the trial sponsor in finding the optimal trial strategies according to their beliefs about the true treatment effect.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.555104  DOI: Not available
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