Bayesian adaptive designs for non-inferiority and dose selection trials.

The process of conducting a pharmaceutical clinical trial often produces information in a way that can be used as the trial progresses. Bayesian methods offer a highly flexible means of using such information yielding inferences and decisions that are consistent with the laws of probability and consequently admit ease of interpretation. Bayesian adaptive sampling methods offer the potential to accelerate the investigation of a drug without compromising the safety of the trial's participants. These methods select a patient's treatment based upon prior information and the knowledge accrued from the trial to date which can reduce patient exposure to unsafe or ineffective treatments and therefore improve patient care in clinical trials. Improving the process of clinical trials in this manner is beneficial to all involved including the pharmaceutical companies and more especially the patients; safer and less expensive drugs can make it to market faster. In this research we present a Bayesian approach to determining if an experimental treatment is non-inferior to an active control treatment within a clinical trial that includes a placebo arm. We incorporate this non-inferiority model in a Bayesian adaptive design that uses joint posterior predictive probabilities of safety and efficacy to determine adaptive allocation probabilities. Results from a retrospective study and a simulation are used to illustrate use of the method. We also present a Bayesian adaptive approach to dose selection that uses effect sizes of doses relative to placebo to perform adaptive allocation and to select the most efficacious dose. The proposed design removes treatment arms if their performance relative to placebo or other treatment arms is undesirable. Results from analyses of simulated data will be discussed.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2006

Author dc:creator

• Spann, Melissa Elizabeth.

Advisor dc:contributor.advisor

• Seaman, John Weldon, Jr., 1956-

• Bayesian statistical decision theory.
• Drugs -- Testing -- Statistical methods.

Statement dc:rights

• Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/4207

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/4207