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Bayes methods in group sequential clinical trials

Qian, Wendi (1997) Bayes methods in group sequential clinical trials. Doctor of Philosophy (PhD) thesis, University of Kent. (doi:10.22024/UniKent/01.02.94593) (KAR id:94593)

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Bayesian methods for group sequential clinical trials have received increasing at­tention recently. They offer an approach for dealing with many difficult problems and have some practical advantages over frequentist methods. This thesis covers Bayesian methods for group sequential clinical trials comparing two treatments using both the Bayes sequential procedure and the Bayes sequential decision pro­cedure. The main outcome measures for clinical trials are distributed as normal, binomial, and exponential and the proportional hazard model for survival time data. Under the framework of Bayes sequential procedure for group sequential clini­cal trials, the student t prior distribution for the parameter of interest is proposed as a replacement for the normal prior distribution when the sample mean is very distant from the mean of the prior distribution. The framework of Bayes sequen­tial procedure in clinical trials on normal distribution responses with variance unknown is given. Bayes sequential decision theory is applied to group sequential clinical trials. First, Bayes sequential decision procedures with piecewise continuous loss func­tions are used in clinical trials on normal distribution responses. The procedures with loss functions which consider treatment efficacy and patient horizon are then given in clinical trials on binary responses. Approximation methods of Bayes sequential decision procedures are explored in clinical trials with survival time data. Robust Bayes analysis in clinical trials is presented to address the criticism on the subjective prior distribution for parameters of interest.

Item Type: Thesis (Doctor of Philosophy (PhD))
DOI/Identification number: 10.22024/UniKent/01.02.94593
Additional information: This thesis has been digitised by EThOS, the British Library digitisation service, for purposes of preservation and dissemination. It was uploaded to KAR on 25 April 2022 in order to hold its content and record within University of Kent systems. It is available Open Access using a Creative Commons Attribution, Non-commercial, No Derivatives ( licence so that the thesis and its author, can benefit from opportunities for increased readership and citation. This was done in line with University of Kent policies ( If you feel that your rights are compromised by open access to this thesis, or if you would like more information about its availability, please contact us at and we will seriously consider your claim under the terms of our Take-Down Policy (
Uncontrolled keywords: Medicine
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
R Medicine > R Medicine (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
SWORD Depositor: SWORD Copy
Depositing User: SWORD Copy
Date Deposited: 21 Jun 2022 14:23 UTC
Last Modified: 17 Jul 2023 09:27 UTC
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