Event

Dynamic treatment regimens (DTRs) are sequential decision rules that specify how to adapt the type, dosage and timing of treatment according to an individual patient’s time-varying characteristics. DTRs offer a framework for operationalizing the multistage decision making in personalized clinical practice, thus providing an opportunity to improve it. They are particularly useful for the management of chronic diseases. Constructing “evidence-based” DTRs from patient data requires implementation of cutting-edge study design and analysis tools. In this talk, I will discuss the key ideas governing the paradigm of DTRs, a novel class of study designs called sequential multiple assignment randomized trial (SMART), and a regression-based analysis approach called Q-learning. The methodological developments will be illustrated through various study examples.