Beyond the average treatment effect: Risk-based approaches to medical decision making

The overall aim of this thesis is to explore the use of baseline risk prediction models as the basis for medical decision making. We study and apply methods for the evaluation of treatment effect heterogeneity in both clinical trial data and observational data. More specifically, we systematically review the existing literature on predictive approaches to the evaluation of heterogeneity of treatment effect, develop scalable and reproducible risk-based predictive approaches to the assessment of treatment effect heterogeneity, and apply risk-based methods to better guide medical decisions.