Research Interests
Statistical precision medicine is an analytic framework that aims to build evidence for matching the right treatment to the right patient at the right time. It's statistical in the sense that we try to learn how to do this from data and might be formalized through a treatment algorithm or dynamic treatment regime. My interest is in bridging the gap between the formal statistical objects we learn and the actual use of dynamic treatment regimes in healthcare decision making and shared healthcare decision making. My work focuses on generating optimal data for statistical precision medicine through innovative clinical trials and developing statistical methods for translational precision medicine.
The tools I use include: Bayesian methods, Bayesian and non-Bayesian machine learning, causal inference, reinforcement learning, and topic modeling.
The tools I use include: Bayesian methods, Bayesian and non-Bayesian machine learning, causal inference, reinforcement learning, and topic modeling.
External Relationships
- Saint Louis University
This faculty member (or a member of their immediate family) has reported outside activities with the companies, institutions, or organizations listed above. This information is available to institutional leadership and, when appropriate, management plans are in place to address potential conflicts of interest.