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Professor Susan lecture
Professor Susan Lecture New
PROFESSOR SUSAN MURPHY, HARVARD UNIVERSITY 14-24 AUGUST 2018
Speaker
SUSAN MURPHY
PROFESSOR OF STATISTICS AT HARVARD UNIVERSITY
Susan A. Murphy is Professor of Statistics, Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences and Radcliffe Alumnae Professor at the Radcliffe Institute at Harvard University. Her lab focuses on improving sequential, individualised, decision making in health, in particular on clinical trial design and data analysis to inform the development of personalised just-in-time adaptive interventions in mobile health. Her work is funded by the National Institutes of Health, USA.
Susan is a Fellow of the Institute of Mathematical Statistics, a Fellow of the College on Problems in Drug Dependence, a former editor of the Annals of Statistics, a member of the US National Academy of Sciences, a member of the US National Academy of Medicine and a 2013 MacArthur Fellow.
Schedule
| DATE | TIME | HOST | UNIVERSITY |
|---|---|---|---|
| William Trad | Powers of Maximal Monotone Operators | Dr Daniel Hauer | The University of Sydney |
| William Trad | Powers of Maximal Monotone Operators | Dr Daniel Hauer | The University of Sydney |
| William Trad | Powers of Maximal Monotone Operators | Dr Daniel Hauer | The University of Sydney |
Talk abstracts
PUBLIC LECTURE
OPTIMISING MOBILE HEALTH INTERVENTIONS
Mobile devices along with wearable sensors allow us to deliver supportive treatments, anytime and anywhere. Mobile interventions are transforming treatments and preventative health management, including support for HIV medication adherence, assisting recovery in addictions and encouraging physical activity and healthy eating. The question remains ‘When and in which contexts, is it most useful to deliver treatments to the user?’ Using data, we can determine if key factors such as location, stress, time of day, mood, ambient noise and so on, impact when and where these treatments are most useful. This talk concerns a new clinical trial design: the micro-randomised trial and associated data analytics for use in addressing this question. The talk will use multiple mobile health studies including the study ‘HeartSteps: A Physical Activity Mobile Intervention’ to illustrate the ideas.
SPECIALIST LECTURE – TOPIC 1
STRATIFIED MICRO-RANDOMISED TRIALS WITH APPLICATIONS IN MOBILE HEALTH
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 randomisation algorithm that ensures that each user is randomised to each treatment an appropriate number of times per day. We develop the stratified micro-randomised trial which involves not only the randomisation 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.