Event Date:
08/20/2021
Description:
Title: Bayesian Model Averaging of Longitudinal Dose-Response Models
Presenter: Richard Payne, PhD (Eli Lilly and Company)
Abstract: Selecting a clinically beneficial and safe dose (or doses) is one of the most difficult and consequential decisions in the drug development process. Dose justification often relies on dose-response modeling, but parametric assumptions must be made a priori, often with little information, potentially leading to a situation where the chosen model does not fit the data well. This is particularly problematic in longitudinal dose-response models, where additional parametric assumptions must be made on the longitudinal trajectory of each dose. This presentation proposes a class of longitudinal dose-response models to be used in the Bayesian model averaging paradigm which can improve trial operating characteristics while maintaining flexibility a priori. The benefits and trade-offs of the proposed method are demonstrated through a simulation study.
Author bio: Richard Payne is a research scientist at Eli Lilly in the Statistical Innovation Center. He specializes in Bayesian methodology, R package development, and trial simulation. Richard graduated with a PhD in statistics from Texas A&M University in 2018.
Slides will be stored in http://www.bayesianscientific.org/kol-lecture-series/
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