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Data Science for Dynamic Intervention Decision-making Lab


Daniel Almirall

Research Associate Professor
Dept of Statistics & Institute for Social Research
University of Michigan
I am particularly interested in developing statistical methods that can be used to form adaptive interventions, sometimes known as dynamic treatment regimes. An adaptive intervention is a sequence of individually tailored decisions rules that specify whether, how, or when–and importantly, based on which measures–to alter the intensity, type, or delivery of treatment at critical decision points during intervention. Adaptive interventions are particularly well-suited for the management of chronic diseases, but can be used in any clinical or educational setting in which sequential medical decision making is essential for the welfare of the individual. They hold the promise of enhancing clinical practice by flexibly tailoring treatments or interventions to individuals when they need it most, and in the most appropriate dose, thereby improving the efficacy and effectiveness of treatment. In health settings, adaptive interventions represent one important tool in the practice of “precision medicine”. However, adaptive interventions can also be used to adapt interventions at the organizational level, for example, to encourage clinics or schools to adopt an evidence-based intervention. I devote a great portion of my time to addressing methodological issues in the design of sequential multiple assignment randomized trials (SMARTs), and other randomized trial designs, that can be used to optimize or evaluate adaptive interventions.
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