Practical Implementation of Bayesian Approaches to Adaptive Trial Design
Featuring David Pober, Associate Director of Biostatistics at PHASTAR
Abstract: 'Practical Implementation of Bayesian Approaches to Adaptive Trial Design'.
Bayesian approaches to adaptive clinical trial design have attractive properties and there is broad desire to apply them in clinical practice. Adoption of these methods may be limited because there is often a gulf between their theoretical benefits and the tools and understanding that are required to implement them effectively. We will discuss a few cases of implementing Bayesian methods in early phase oncology trials supported by PHASTAR, and investigate some software resources that are available to deploy these methods in the field.
About the speaker:
David Pober is the Associate Director of Biostatistics for PHASTAR's Boston office. He earned his M.S. and Ph.D. from the University of Massachusetts, Amherst, where his work focused on the application of machine learning algorithms to the analysis of human physical activity data. David has been working with SAS, in particular linear mixed effects models, for more than twenty years, and for the last ten years has served as lead statistician in the CRO and healthcare spaces on clinical trials at all phases, most recently at the Joslin Diabetes Center in Boston.