Bayesian Adaptive Designs For Proof-of-Concept Trials and Platform Trials

<p>With the revolutionary achievement in molecular targeted therapies and cancer immunotherapies, the traditional drug development paradigm in phase II trials becomes increasingly inefficient due to its slow progress, high cost, and high failure rate. Fitting one standard strategy to all different trials also harms its reliability in decision-making because it doesn’t fully use all available resources and information in each trial. It’s crucial to develop novel phase II trial designs to accomplish different objectives for different types of trials. This research mainly focuses on Bayesian adaptive designs for phase II trials. Three types of trials are discussed in which traditional designs may encounter limitations, and three novel designs have been proposed to resolve these issues. Specifically, chapter 2 focused on randomized phase II trials. Compared to traditional single-arm trials, randomized trials could provide more consistent results even when bias in historical control is introduced. We proposed s Bayesian optimal design that has the capability of dealing with single and mixed endpoints for randomized trials. This design is optimized to maximize power while controlling type I error rate. Chapter 3 focused on platform trials. A platform trial could simultaneously screen multiple treatments on multiple indications, which reduces the length of the trial and saves operational resource. We proposed a Bayesian platform trial design that enables borrowing of information across subtrials with multiple primary endpoints. Chapter 4 discussed phase II trials with dual-criteria decision-making rules. Hypothesis testing in traditional trial designs could only show whether the investigational treatment is statistically better than historical control, which may not be sufficient to claim treatment is clinically effective. We proposed a Bayesian optimal design that takes both statistical significance and clinical significance into consideration. All these three proposed designs outperform the traditional designs by improving trial efficiency and providing desirable operating characteristics.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation (PhD)

Year dc:date.available

2022

Authors dc:creator

• Zhao, Yujie
• <p>0000-0003-4450-5804</p>

Contributors dc:contributor

• Ying Yuan
• Amir A. Jazaeri
• J. Jack Lee

• Bayesian adaptive design
• phase II clinical trial
• platform trial design
• Biostatistics

Repository record dc:identifier

https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1204

OAI identifier oai:identifier

oai:digitalcommons.library.tmc.edu:utgsbs_dissertations-2261