Detection of treatment effects by covariate-adjusted expected shortfall

TL;DR

This paper introduces a covariate-adjusted expected shortfall test that enhances the detection of treatment effects in distribution tails, offering greater power and requiring smaller sample sizes compared to traditional mean-based tests.

Contribution

The paper proposes a novel covariate-adjusted expected shortfall test that improves power in tail effect detection over conventional mean-based methods.

Findings

The new test achieves higher power in tail effect detection.

It reduces required sample sizes significantly.

Demonstrated effectiveness in rheumatoid arthritis treatment data.

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.