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December 2010 Detection of treatment effects by covariate-adjusted expected shortfall
Xuming He, Ya-Hui Hsu, Mingxiu Hu
Ann. Appl. Stat. 4(4): 2114-2125 (December 2010). DOI: 10.1214/10-AOAS347

Abstract

The statistical tests that are commonly used for detecting mean or median 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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Xuming He. Ya-Hui Hsu. Mingxiu Hu. "Detection of treatment effects by covariate-adjusted expected shortfall." Ann. Appl. Stat. 4 (4) 2114 - 2125, December 2010. https://doi.org/10.1214/10-AOAS347

Information

Published: December 2010
First available in Project Euclid: 4 January 2011

zbMATH: 1220.62137
MathSciNet: MR2829949
Digital Object Identifier: 10.1214/10-AOAS347

Keywords: CVaR , expected shortfall , quantile , Total Sharp Score

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 4 • December 2010
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