Speaker: Bibhas Chakraborty, PhD
Adjunct Associate Professor of Biostatistics and Bioinformatics
Duke University, Durham, NC
Mobile health (mHealth) interventions (e.g., motivational text-messages or nudges to promote healthy behaviors) are becoming increasingly common in tandem with advances in mobile and wearable sensor technologies. In this talk, we will discuss an innovative experimental design arising in mHealth, namely, the micro-randomized trial (MRT) that involves sequential, within-person randomization over many instances. The basic MRT design can be further improved to make it adaptive, thereby enabling it to learn from accumulated data as the trial progresses. This is appealing from an ethical perspective since the adaptive learning tends to make better interventions available to the trial participants. Adaptive learning in such trial designs is often operationalized via Reinforcement Learning algorithms. Specifically, we will discuss the role of a particular algorithm called Thompson sampling in designing adaptive MRTs. Theoretical and simulation results will be shown to validate the proposed approach. Real mHealth trials will be discussed as case studies.
The Department of Epidemiology & Biostatistics welcomes all participants to our events. If you need a reasonable accommodation to participate in this event because of a disability, please contact Liz Buggs ([email protected]).