Principal Investigator, The Methodology Center
H.E. Robbins Professor, Statistics
Research Professor, The Institute for Social Research
Department of Statistics
439 West Hall, 1085 S. Univ.
University of Michigan
Ann Arbor, MI 48109-1107
Ph.D., University of North Carolina, 1989 (Statistics)
B.S., Louisiana State University, 1980 (Mathematics)
Research Interests & Collaborations
I am working on multiple related projects all aimed at how best to collect and use data in the development of adaptive interventions. My collaborators on these projects are Daniel Almirall, Linda Collins, and Inbal Nahum-Shani.
My current primary interest concerns clinical trial design and the development of data analytic methods for informing multi-stage decision making in health. In particular for (1) constructing individualized sequences of treatments (known as adaptive interventions) for use in informing clinical decision making and (2) constructing real-time individualized sequences of treatments (known as just-in-time adaptive interventions) delivered by mobile devices. (See workshop on just in time adaptive interventions.) Adaptive interventions, also known as dynamic treatment regimes, are composed of a sequence of decision rules that specify when to alter the therapy and specify which intensity or type of subsequent therapy should be offered. These regimes hold the promise of maximizing treatment efficacy by avoiding ill effects due to over-treatment and by providing increased treatment levels to those who can benefit.
Recent Honors & Awards
2014: Elected a Member of the Institute of Medicine
2014: Elected a Fellow of the College of Problems of Drug Dependence
2013: MacArthur Fellowship
2011: Elected a Member of the International Statistical Institute
Adaptive Interventions for Minimally Verbal Children With ASD in the Community
Eunice Kennedy Shriver National Institute for Child Health and Human Development, R01 HD073975
2012-present; Role: Co-I (PI: C. Kasari)
Improving Mental Health Outcomes: Building an Adaptive Implementation Strategy
National Institute of Mental Health, R01 MH099898
2014-2018; Role: Co-I (PI: A. Kilbourne)
University of Memphis Centers of Excellence for Big Data Computing in the Biomedical Sciences
National Institute of Biomedical Imaging and Bioengineering
2014-2018; Role: Project Leader of Michigan Center (PI: S. Kumar)
HeartSteps: Development and Evaluation of a Personalized, Adaptive mHealth Intervention for Physical-Activity Maintenance Following Phase II Cardiac Rehabilitation
National Heart, Lung, and Blood Institute R01 HL125440
Role: Co-I (PI: Klasnja)
Bekiroglu, K., Lagoa, C., Murphy, S., & Lanza, S. T. (in press). A robust MPC approach to the design of treatments. Proceedings of the 52nd IEEE Conference on Decision and Control.
Gunlicks-Stoessel, M., Mufson, L., Westervelt, A., Almirall, D., & Murphy, S.A. (in press). A Pilot SMART for Developing an Adaptive Treatment Strategy for Adolescent Depression. Journal of Clinical Child and Adolescent Psychology.
Almirall, D., Griffin, B. A., McCaffrey, D. F., Ramchand, R., Yuen, R. A., & Murphy, S. (2014). Time-varying effect moderation using the structural nested mean model: Estimation using inverse-weighted regression with residuals. Statistics in Medicine, 33, 3466-3487.
Almirall, D., Nahum-Shani, I., Sherwood, N. E., & Murphy, S. A. (2014). Introduction to SMART designs for the development of adaptive interventions: With application to weight loss research. Translational Behavioral Medicine, 43, 260-274.
Chakraborty, B., & Murphy, S. A. (2014). Dynamic treatment regimes. Annual Review of Statistics and its Application, 1, 447-464.
Laber, E.B., Lizotte, D.J., Qian, M., Pelham, W.E., & Murphy, S.A. (2014). Dynamic treatment regimes: Technical challenges and applications. Electron J Stat, 8(1), 1225-1272.
Lagoa, C., Bekiroglu, K., Lanza, S. T., & Murphy, S. (2014). Designing adaptive intensive interventions using methods from engineering. Journal of Consulting and Clinical Psychology, 82, 868-878.
Shortreed, S. M., Laber, E., Stroup, T. S., Pineau, J., & Murphy, S. A. (2014). A multiple imputation strategy for sequential multiple assignment randomized trials. Statistics in Medicine, 33(24), 4202-14.
Almirall, D., Griffin, B. A., McCaffrey, D. F., Ramchand, R., Yuen, R. A., & Murphy, S. A. (2013). Time-varying effect moderation using the structural nested mean model: Estimation using inverse-weighted regression with residuals. Statistics in Medicine. Advance online publication. doi: 10.1002/sim.5892
Kumar, S., Nilsen, W.J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., Riley, W. T., Shar, A., Spring, B., Spruijt-Metz, D., Hedeker, D., Honavar, V., Kravitz, R., Craig Lefebvre, R., Mohr, D.C., Murphy, S.A., Quinn, C., Shusterman, V., Swendeman, D. (2013). Mobile health technology evaluation, the mHealth evidence workshop. American Journal of Preventive Medicine, 45(2), 228-236. PMC Journal-In Process
Nahum-Shani, I., Xi, L., Henderson, M. M., & Murphy, S. A. (2013). An innovative experimental design for addressing the “assistance dilemma” using interactive learning environments. In S. A. Karabenick & M. Puustinen (Eds), Advances in help-seeking research and applications: The role of emerging technologies. Charlotte, NC: Information Age Publishing.
Qian, M., Nahum-Shani, I., Murphy, S. A. (2013). Dynamic treatment regimes. In X. Tu and W. Tang (Eds.), Modern clinical trial analysis. New York, NY: Springer Science.