Reinforcement Learning Lab | Research ]
[ Grants | Seminars/Workshops/Videos/Interviews | SMART Clinical Trials | Micro-Randomized Trials ]
[ Harvard Stat Dept | Harvard CS Dept | Radcliffe Institute for Advanced Studies ]
[ Havard Home Page | The Methodology Center | MD2K | Resume | Google Scholar ]
[ News about our Student Team's entry into the National Institute of Drug Abuse Mobile App Challenge!! ]
[ Sample Size Calculator for Micro-Randomized Trials ]
My current primary interest concerns clinical trial design and the development of data analytic methods for informing multi-stage decision making in health. In particular for (1) constructing individualized sequences of treatments (a.k.a., adaptive interventions) for use in informing clinical decision making and (2) constructing real time individualized sequences of treatments (a.k.a., Just-in-Time Adaptive Interventions) delivered by mobile devices. Adaptive Interventions are composed of a sequence of decision rules that specify when to alter the therapy and specify which intensity or type of subsequent therapy should be offered. The decision rules employ variables such as patient response, risk, burden, adherence, and preference, collected during prior therapy. These regimes hold the promise of maximizing treatment efficacy by avoiding ill effects due to over-treatment and by providing increased treatment levels to those who can benefit. Check out our MD2K mobile health book!
Our lab's work is funded by National Institute on Drug Abuse , by National Institute on Alcohol Abuse and Alcoholism, by National Heart, Lung and Blood Institute and by National Institute of Biomedical Imaging and Bioengineering. I work with researchers at Quantitative Methodology Program (ISR), The Methodology Center and Mobile Sensor Data-to-Knowledge on these topics.Susan A. Murphy,