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Abstract Details
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Activity Number:
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452
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #302309 |
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Title:
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Modified Covariate Approach for Personalized Medicine
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Author(s):
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Lu Tian*+
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Companies:
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Stanford University
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Address:
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Redwood Building, Palo Alto, CA, 94303,
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Keywords:
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personalized medicine ;
dimension reduction ;
treatment-covariate interaction
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Abstract:
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It is important to stratify patient population such that the treatment effect is different for different subgroups of patients. The stratification can be used to select the optimal treatment for individual patients in a given subgroup. Statistically speaking, it amount to identify treatment and covariate interactions. This is a challenging task especially when the dimension of covariate is high. We propose a novel approach to directly estimate the covariate-treatment interaction without the need for modeling the main effect. Therefore, the method is very appealing for cases where the high-dimensional covariate such as gene expression levels is involved. We provide some theoretical justifications from casual inference perspective and illustrate the proposed with real data examples.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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