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Abstract Details
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Activity Number:
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211
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #303899 |
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Title:
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Constructing Confidence Sets for the Optimal Dynamic Regime
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Author(s):
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Sherri Rose*+ and Tuo Zhao and Julieta Molina and Andrea Rotnitzky and Han Liu and Michael Rosenblum
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Companies:
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Johns Hopkins Bloomberg School of Public Health and The Johns Hopkins University and Universidad Torcuato di Tella and Di Tella University/Harvard School of Public Health and The Johns Hopkins University and Johns Hopkins Bloomberg School of Public Health
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Address:
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615 N Wolfe Street, Baltimore, MD, , USA
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Keywords:
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causal inference ;
observational data ;
confidence intervals ;
dynamic treatment regimes
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Abstract:
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In many practical applications, treatment assignment depends on previous covariates and prior treatment. It may be beneficial to define treatment "rules" (otherwise referred to as dynamic treatment regimes) in order to identify optimal patient outcomes. We build on a recent paper (Orellana, Rotnitzky & Robins 2010) to develop and implement an algorithm for construction of valid confidence regions for the optimal dynamic treatment regime using linear programming.
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