Micro-randomized trials are trials in which individuals are randomized 100s or 1000s of times over the course of the study. The goal of these trials is to assess the impact of momentary interventions (e.g., interventions that are intended to impact behavior over small time intervals). A fast-growing area of mHealth concerns the use of mobile devices for collecting real-time data, processing this data, and providing momentary interventions. We discuss the design and analysis of these types of trials.
The BIDS Data Science Lecture Series is co-hosted by BIDS and the Data, Science, and Inference Seminar.
Speaker(s)
Susan Murphy
Susan Murphy’s research focuses on improving sequential, individualized, decision making in health, in particular on clinical trial design and data analysis to inform the development of adaptive interventions (e.g., treatment algorithms). She is a leading developer of the Sequential Multiple Assignment Randomized Trial (SMART) design, which has been and is being used by clinical researchers to develop adaptive interventions in depression, alcoholism, ADHD treatment, substance abuse, HIV treatment, obesity, diabetes, and autism. She collaborates with clinical scientists, computer scientists, and engineers and mentors young clinical scientists on developing adaptive interventions. Susan is currently working as part of several interdisciplinary teams to develop clinical trial designs and learning algorithms for settings in which patient information is collected in real time (e.g., via smart phones or other wearable devices), and thus sequences of interventions can be individualized online. She is a Fellow of the College on Problems in Drug Dependence, a former editor of the Annals of Statistics, a member of the Institute of Medicine, and a 2013 MacArthur Fellow.