4.5 Article

On the relative efficiency of the intent-to-treat Wilcoxon-Mann-Whitney test in the presence of noncompliance

Journal

BIOMETRIKA
Volume 109, Issue 3, Pages 873-880

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/biomet/asab053

Keywords

Causal inference; Instrumental variable; Nonignorable missingness; Pitman efficiency; Randomized controlled trial; t test

Funding

  1. U.S. National Science Foundation
  2. National Institutes of Health

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In this paper, a general framework is established to study the asymptotic properties of the intent-to-treat Wilcoxon-Mann-Whitney test in randomized experiments with nonignorable noncompliance. The Pitman efficiencies of the intent-to-treat Wilcoxon-Mann-Whitney and t tests are derived under location-shift alternatives. It is shown that the former is superior if the compliers are more likely to be found in high-density regions or if the noncompliers tend to reside in the tails. The results can help researchers choose the more efficient test for existing data and calculate sample size for future trials with noncompliance.
A general framework is set up to study the asymptotic properties of the intent-to-treat Wilcoxon-Mann-Whitney test in randomized experiments with nonignorable noncompliance. Under location-shift alternatives, the Pitman efficiencies of the intent-to-treat Wilcoxon-Mann-Whitney and t tests are derived. It is shown that the former is superior if the compliers are more likely to be found in high-density regions of the outcome distribution or, equivalently, if the noncompliers tend to reside in the tails. By logical extension, the relative efficiency of the two tests is sharply bounded by least and most favourable scenarios in which the compliers are segregated into regions of lowest and highest density, respectively. Such bounds can be derived analytically as a function of the compliance rate for common location families such as Gaussian, Laplace, logistic and t distributions. These results can help empirical researchers choose the more efficient test for existing data, and calculate sample size for future trials in anticipation of noncompliance. Results for nonadditive alternatives and other tests follow along similar lines.

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