ZOOM LINK
Meeting ID: 944 1693 8471  
Passcode: 681037

Cardiovascular diseases are considered the leading cause of death in the US and worldwide. The management of cardiovascular diseases can be improved by (1) providing physicians and their patients with flexibility in the implementation of protocols, and (2) incorporating novel procedures, such as genetic testing. To benefit from physicians’ opinions and allow for patients’ preferences in the implementation of mathematical models, I introduce a framework that integrates approximate dynamic programming and statistical multiple comparisons to obtain sets of near-optimal treatment choices. By analyzing the structure of the sets, I characterize their behavior with respect to the modeling data and identify when they can be ordered according to the context of the problem. I show how this method can be applied in medical practice by finding hypertension treatment plans for 16.72 million adults in the US.

To understand the clinical and policy implications of genetic testing in cardiovascular diseases, I next present a thoroughly validated simulation model to evaluate the impact of genetic information across different populations in the US. Building upon this work, I illustrate a framework that combines dynamic programming with value of information analysis to simultaneously determine optimal cholesterol treatment and genetic testing decisions. To conclude my talk, I will discuss future opportunities at the intersection of operations research and statistics to support medical decision making in practice.