$0 – $12

Stratified Micro-Randomized Trials with Applications in Mobile Health

Event Information

Share this event

Date and Time

Location

Location

Room E51-149

MIT Tang Center

70 Memorial Drive

Cambridge, MA

View Map

Event description

Description

Stratified Micro-Randomized Trials with Applications in Mobile Health

Professor Susan Murphy

Co-Sponsors: Boston Chapter of the ASA; Department of Mathematics & Statistics at Boston University; and the IDSS Institute at MIT

Time: Reception and light Dinner 6:15 pm; Presentation 7:00 pm
Location: See http://web.mit.edu/eventguide/cacfacilities/tang.html


Registration: Requested by 10 am, November 12, 2018; Space is limited so please register early.
Cost: Dinner, $7 for students; $12 for non-students;
Presentation: free

Abstract:
Technological advancements in the field of mobile devices and wearable sensors make it possible to deliver treatments anytime and anywhere to users like you and me. Increasingly the delivery of these treatments is triggered by detections/predictions of vulnerability and receptivity. These observations are likely to have been impacted by prior treatments. Furthermore, the treatments are often designed to have an impact on users over a span of time during which subsequent treatments may be provided. Here we discuss our work on the design of a mobile health smoking cessation study in which the above two challenges arose. This work involves the use of multiple online data analysis algorithms. Online algorithms are used in the detection, for example, of physiological stress. Other algorithms are used to forecast at each vulnerable time, the remaining number of vulnerable times in the day. These algorithms are then inputs into a randomization algorithm that ensures that each user is randomized to each treatment an appropriate number of times per day. We develop the stratified micro-randomized trial which involves not only the randomization algorithm but a precise statement of the meaning of the treatment effects and the primary scientific hypotheses along with primary analyses and sample size calculations. Considerations of causal inference and potential causal bias incurred by inappropriate data analyses play a large role throughout.

Speaker Biography:
Susan Murphy is Professor of Statistics at Harvard University, Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University, and Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. Her current research interests concern clinical trial design and the development of data analytic methods for informing multi- stage decision making in health, particularly in mobile health. She is a 2013 MacArthur Fellow, a member of the National Academy of Sciences and the National Academy of Medicine, both of the US National Academies. She is currently president of the Bernoulli Society and incoming president of the Institute for Mathematical Statistics.

Share with friends

Date and Time

Location

Room E51-149

MIT Tang Center

70 Memorial Drive

Cambridge, MA

View Map

Save This Event

Event Saved