Abstract Details
Activity Number:
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240
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #312962
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Title:
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Sequential Advantage Selection for Optimal Treatment Regime
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Author(s):
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Ailin Fan*+ and Wenbin Lu and Rui Song
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Companies:
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North Carolina State University and North Carolina State University and North Carolina State University
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Keywords:
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optimal treatment regime ;
variable selection ;
sequential selection ;
prescriptive variables
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Abstract:
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Variable selection for decision-making related to the optimal treatment regime in a clinical trial or an observational study is getting more attention. Current variable selection techniques focused on selecting variables that help make a good prediction. However, some variables that are poor in prediction but are critical for decision-making may be ignored, and hence new variable selection method is needed for decision-making. Gunter et al. (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually. This is a quantity indicating the importance of each variable for decision-making. In this article, we developed a sequential advantage selection method based on the S-score. Our method selects qualitatively interacted variables sequentially, and hence excludes marginally important but jointly unimportant variables. What's more, with the proposed stopping criteria, our method can handle a large amount of covariates even if sample size is small. Simulation results show our method performs well in practical settings. We further applied our method to data from a clinical trial for depression.
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Authors who are presenting talks have a * after their name.
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