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Call for papers: Conference on computational social science, April 2017, U-M

Sioban Harlow honored with 2017 Sarah Goddard Power Award for commitment to women's health

Post-doc fellowship in computational social science for summer or fall 2017, U-Penn

ICPSR Summer Program scholarships to support training in statistics, quantitative methods, research design, and data analysis

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Mon, Feb 13, 2017, noon:
Daniel Almirall, "Getting SMART about adaptive interventions"

A Strategy for Optimizing and Evaluating Behavioral Interventions

Publication Abstract

Collins, L.M., Susan A. Murphy, V. Nair, and V. Strecher. 2005. "A Strategy for Optimizing and Evaluating Behavioral Interventions." Annals of Behavioral Medicine, 30(1): 66-73.

Background. This article suggests a multiphase optimization strategy (MOST) for achieving the dual goals of program optimization and program evaluation in the behavioral intervention field. Methods. MOST consists of the following three phases: (1) screening, in which randomized experimentation closely guided by theory is used to asses an array of program and/or delivery components and select the components that merit further investigation; (2) refining, in which interactions among the identified set of components and their interrelationships with covariates are investigated in detail, again via randomized experiments, and optimal dosage levels and combinations of components are identified; and (3) confirming, in which the resulting optimized intervention is evaluated by means of a standard randomized intervention trial. In order to make the best use of available resources, MOST relies on design and analysis tools that help maximize efficiency, such as fractional factorials. Results. A slightly modified version of an actual application of MOST to develop a smoking cessation intervention is used to develop and present the ideas. Conclusions. MOST has the potential to husband program development resources while increasing our understanding of the individual program and delivery components that make up interventions. Considerations, challenges, open questions, and other potential

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