Prerequisites: PhD level background in probability theory and statistical inference, basic knowledge of statistical models, programming skills
Goal: This course will provide a comprehensive introduction to methodology for data-based development and evaluation of dynamic treatment regimes. A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key point in a disease or disorder process at which a decision on the next treatment action must be made. Each rule takes patient information to that point as input and returns the treatment s/he should receive from among the available options, thus tailoring treatment decisions to a patient’s individual characteristics. Dynamic treatment regimes formalize how clinicians make decisions in practice by synthesizing evolving information on a patient and are thus of considerable importance in precision medicine. Dynamic treatment regimes are also relevant in other contexts in which sequential decisions on interventions or policies must be made, as in education, engineering, economics and finance, and resource management.
Methods for estimation of dynamic treatment regimes and in particular optimal treatment regimes from data will be motivated and developed through a formal time-dependent causal inference framework. The gold standard study design for developing and evaluating regimes is the sequential multiple assignment randomized trial (SMART), considerations for which will be discussed. Inference for optimal treatment regimes is a nonstandard statistical problem and is thus notoriously difficult; an introduction to this challenge will be presented. Examples throughout the course will be drawn from cancer and other chronic disease research and research in the behavioral, educational, and other sciences.
Students completing this course will have a foundation in causal inference and fundamental results and methods for dynamic treatment regimes that will provide the babasis for study of the rapidly evolving literature on dynamic treatment regimes and precision medicine.