Journal
STATISTICS IN MEDICINE
Volume 34, Issue 6, Pages 1012-1030Publisher
WILEY
DOI: 10.1002/sim.6386
Keywords
clinical trials; covariate adjustment; permutation test; power; probabilistic index models
Categories
Funding
- Flemish Fund for Scientific research (FWO Vlaanderen)
- IAP research network from the Belgian government (Belgian Science Policy) [P07/06]
- Flemish Fund for Scientific research (FWO Vlaanderen) [G.0202.14N]
- Ghent University
- Hercules Foundation
- Flemish Government - department EWI
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The Mann-Whitney U test is frequently used to evaluate treatment effects in randomized experiments with skewed outcome distributions or small sample sizes. It may lack power, however, because it ignores the auxiliary baseline covariate information that is routinely collected. Wald and score tests in so-called probabilistic index models generalize the Mann-Whitney U test to enable adjustment for covariates, but these may lack robustness by demanding correct model specification and do not lend themselves to small sample inference. Using semiparametric efficiency theory, we here propose an alternative extension of the Mann-Whitney U test, which increases its power by exploiting covariate information in an objective way and which lends itself to permutation inference. Simulation studies and an application to an HIV clinical trial show that the proposed permutation test attains the nominal Type I error rate and can be drastically more powerful than the classical Mann-Whitney U test. Copyright (C) 2014 John Wiley & Sons, Ltd.
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