Bayesian approaches for survival data in pharmaceutical research.

In this research, we consider Bayesian methodologies to address problems in biopharmaceutical research, most of which are motivated by real-world problems in network meta-analysis, prior elicitation, and adaptive designs. Network meta-analysis is a hierarchical model used to combine the results of multiple studies, and allows for us to make direct and indirect comparisons between treatments. We investigate Bayesian network meta-analysis models for survival data based on modeling the log-hazard rates, as opposed to hazards ratios. Expert opinion is often needed to construct priors for time-to-event data, especially in pediatric and oncology studies. For this, we propose a prior elicitation method for the Weibull time-to-event distribution that is based on potentially observable time-to-event summaries which can be transformed to obtain a joint prior distribution for the Weibull parameters. Bayesian adaptive designs take advantage of accumulating information, by allowing key trial parameters to change in response to accruing information and predefined rules. We introduce a novel model-based Bayesian assessment of reading speed that uses an adaptive algorithm to target key reading metrics. These metrics are used in the assessment of reading speed in individuals with poor vision.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Baylor University.

Year dc:date.issued

2020

Author dc:creator

• Prajapati, Purvi Kishor, 1992-

Advisors dc:contributor.advisor

• Stamey, James D.
• Seaman, John Weldon, Jr., 1956-

• Survival.
• Bayesian.
• Weibull.
• Network meta-analysis.
• Elicitation.
• Adaptive design.

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/2104/12052

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/12052