Empirical Power and Type I Error of Covariate Adjusted Nonparametric Methods

Abstract

In clinical trials, practitioners collect baseline covariates for enrolled patients prior to treatment assignment. In recent guidance from Food and Drug Administration and European Medicines Agency, regulators encourage practitioners to utilize baseline information at the analysis stage to improve the efficiency. However, the current guidance focused on linear or non-linear modelling approach. Nonparametric statistical methods were not focus in the guidance. In this article, we conducted simulations of several covariate-adjusted nonparametric statistical tests. Wilcoxon rank sum test is a widely used method for non-normally distributed response variables between two groups but its original form does not take into account the possible effect of covariates. We investigated the empirical power and the type I error of the Wilcoxon type test statistics under various settings of covariate adjustments commonly encountered in clinical trials. In addition to Wilcoxon type test statistics, we also compared simulation results to more advanced nonparametric test statistics such as the aligned rank test and Jaeckel, Hettmansperger-McKean test. The simulation result shows when there is covariate imbalance, applying Wilcoxon rank sum test without adjusting the covariates will become problematic. The survey of the covariate adjustments for varies tests under investigation gives brief guidance to trial practitioners in real practice, particularly whose baseline covariates are not well balanced.