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?
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.
In this talk, I will provide an overview of these types of models using three real-world examples:
1) Pharmacokinetic model suitable for an early stage trial in small patient populations, particularly in rare diseases
2) Semi-mechanistic joint survival model for targetted therapies in solid tumors
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