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Activity Number: 557 - Affordable Clinical Trials Through Innovative Technology
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Caucus for Women in Statistics
Abstract #326498
Title: Real-Time, Within-Person Randomization Using a Bandit Algorithm in a Clinical Trial
Author(s): Susan Murphy*
Companies: Harvard University
Keywords: online; dynamic treatment regime; experimental design; mobile health

A formidable challenge in designing sequential treatments is to determine when and in which context it is best to deliver treatments. Consider treatment for individuals struggling with chronic health conditions. Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment. That is, the treatment is adapted to the individual's context; the context may include current health status, current level of social support and current level of adherence for example. Data sets on individuals with records of time-varying context and treatment delivery can be used to inform the construction of the decision rules. There is much interest in personalizing the decision rules, particularly in real time as the individual experiences sequences of treatment. Here we discuss our work in designing online "bandit" learning algorithms for use in personalizing mobile health interventions.

Authors who are presenting talks have a * after their name.

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