Estimations of treatment effects based on covariate adjusted nonparametric methods

Abstract

Abstract Nonparametric tests are commonly used tests for two sample comparison in clinical studies. However, the estimation of treatment effects associated with the tests may not be obvious, especially under the covariate adjustment. In this article, we evaluated the effect of covariate adjustment on estimating treatment effects based on the Wilcoxon Rank Sum test, the van Elteren test, aligned rank test, and Jaeckel, Hettmansperger-McKean test through Monte Carlo simulations via mean square error and coverage probability. Based on the simulation, commonly used ANCOVA-based approach do not have good estimation of treatment effect when the covariate imbalance is severe. Aligned rank test seems perform well across most scenarios.