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Activity Number:
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275
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #304582 |
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Title:
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Detection of Treatment Effects by Covariate-Adjusted Expected Shortfall
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Author(s):
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Ya-Hui Hsu*+ and Xuming He and Mingxiu Hu
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Companies:
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University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign and Millennium Pharmaceuticals/The Takeda Oncology Company
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Address:
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, Champaign, IL, 61820,
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Keywords:
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Expected Shortfall ; Quantile ; Total Sharp Score
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Abstract:
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The statistical tests that are commonly used in detecting treatment effects suffer from low power when the two distribution functions differ only in the upper (or lower) tail, as in the assessment of the Total Sharp Score (TSS) under different treatments for rheumatoid arthritis. In this article, we propose a more powerful test that detects treatment effects through the expected shortfalls. We show how the expected shortfall can be adjusted for covariates, and demonstrate that the proposed test can achieve a substantial sample size reduction over the conventional tests on the mean effects.
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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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