A comparison of nonparametric and parametric methods to adjust for baseline measures

Abstract When analyzing the randomized controlled trial, we may employ various statistical methods to adjust for baseline measures. Depending on the method chosen to adjust for baseline measures, inferential results can vary. We investigate the Type 1 error and statistical power of tests comparing treatment outcomes based on parametric and nonparametic methods. We also explore the increasing levels of correlation between baseline and changes from the baseline, with or without underlying normality. These methods are illustrated and compared via simulations.