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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

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Next Brown Bag

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

Susan A. Murphy photo

Marginal Mean Models for Dynamic Regimes

Publication Abstract

Murphy, Susan A., M.J. Van Der Laan, J.M. Robins, and and Conduct Problems Research Group. 2001. "Marginal Mean Models for Dynamic Regimes." Journal of the American Statistical Association, 96(456): 1410-1423.

A dynamic treatment regime is a list of rules for how the level of treatment will be tailored through time to an individual's changing severity. In general, individuals who receive the highest level of treatment are the individuals with the greatest severity and need for treatment. Thus, there is planned selection of the treatment dose. In addition to the planned selection mandated by the treatment rules, staff judgment results in unplanned selection of the treatment level. Given observational longitudinal data or data in which there is unplanned selection of the treatment level, the methodology proposed here allows the estimation of a mean response to a dynamic treatment regime under the assumption of sequential randomization.

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