Susan A. Murphy

Harvard University


Election Year: 2016
Primary Section: 53, Social and Political Sciences
Secondary Section: 32, Applied Mathematical Sciences
Membership Type: Member

Biosketch

Susan A. Murphy is Professor of Statistics and Computer Science, and a Radcliffe Alumnae Professor at the Radcliffe Institute, all at Harvard University. Her research focuses on improving sequential, individualized, decision making in health, in particular on clinical trial design and data analysis to inform the development of just-in-time adaptive interventions in mobile health. She graduated from Louisiana State University with a degree in mathematics and earned her PhD in Statistics at the University of North Carolina, Chapel Hill in 1989. 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 U.S. National Academy of Sciences, the U.S. National Academy of Medicine and a 2013 MacArthur Fellow.

Research Interests

Susan Murphy’s lab works on how to design trials and analyze the resulting data so as to personalize and adapt sequences of interventions to the individual. This is particularly useful when individuals are suffering from a chronic disorder in which treatment needs to adapted to the individual over time. They developed a new type of randomized trial, the sequential, multiple assignment randomized trial which has now been deployed across many areas of health including the treatment of substance use disorders, attention deficit disorders, depression, alcohol use disorders, obesity, obsessive compulsive disorders, insomnia, bipolar disorders, autism spectrum disorders and also in implementation science to improve the implementation of evidence based mental health treatment. The lab currently focuses on mobile health in which a sequence of in-the-moment supportive interventions might be provided to an individual over time. The lab has developed a randomized trial, the “micro-randomized trial" and associated data analytic methods that can be used to optimize the timing and content of the sequence of supportive interventions. The lab is also working on online data analytic methods for use in personalizing the timing and content of mobile interventions as the individual uses the mobile device and data is collected in real time.

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