Part I: Building Robust PK/PD Population Models with Bayesian Inference
Michael Betancourt (1) and Sebastian Weber (2)
(1) University of Warwick and (2) Novartis Pharma AG
Because clinical data is often limited in the number of patients or observations per patient, PK/PD analyses that don’t model the complexity in the data compromise our ability to make robust inferences, especially when trying to characterize variation amongst a population of patients. In this talk we will discuss how Bayesian inference is the
natural framework for modeling these complexities and building robust PK/PD population models, ending with a contemporary example to demonstrate the power and clinical relevance of this approach.
