Abstract: Precision medicine typically refers to the development of drugs and other interventions for individual patients. But how do you assess efficacy and make predictions in this extreme small data regime? The Bayesian framework is ideal for this type of inference as it allows us to combine population and personal effects in a principled way and make predictions for both groups and individuals. The inferences are further improved when we introduce mechanistically inspired components into the modeling framework. I’ll talk about building pharma models in the small data regime and how we use Stan (a statistical modeling language for Bayesian inference) with R for analysis.
Speaker: Daniel Lee
Title: Bayesian Models for Smaller Trial Sizes
Abstract_Sanitzed: Precision medicine typically refers to the development of drugs and other interventions for individual patients. But how do you assess efficacy and make predictions in this extreme small data regime? The Bayesian framework is ideal for this type of inference as it allows us to combine population and personal effects in a principled way and make predictions for both groups and individuals. The inferences are further improved when we introduce mechanistically inspired components into the modeling framework. I’ll talk about building pharma models in the small data regime and how we use Stan (a statistical modeling language for Bayesian inference) with R for analysis.