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Activity Number: 407
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #316138 View Presentation
Title: Comparing Treatment Policies with Assistance from the Structural Nested Mean Model
Author(s): Xi Lu* and Kevin Lynch and David Oslin and Susan A. Murphy
Companies: University of Michigan and University of Pennsylvania and University of Pennsylvania and University of Michigan
Keywords: Dynamic treatment regime ; Adaptive intervention ; Semiparametric model ; Sequential multiple assignment randomized trial
Abstract:

Treatment policies, also known as dynamic treatment regimes, are sequences of decision rules that link the observed patient history with treatment recommendations. Multiple, plausible, treatment policies are frequently constructed by researchers using expert opinion, theories and reviews of the literature. Often these different policies represent competing approaches to managing an illness. Here we develop an "assisted estimator" that can be used to compare the mean outcome of competing treatment policies. The term "assisted" refers to the fact estimators from the Structural Nested Mean Model, a parametric model for the causal effect of treatment at each time point, are used in the process of estimating the mean outcome. This work is motivated by our work on comparing the mean outcome of two competing treatment policies using data from the ExTENd study in alcohol dependence.


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