A comparison of nonparametric and parametric methods to adjust for baseline measures.
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.
Pfizer Inc., New York, NY, USA; Department of Statistics, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA. Electronic address: Martin.Carlsson@pfizer.com.
Martin O
MO
Carlsson
Pfizer Inc., New York, NY, USA. Electronic address: Kelly.Zou@pfizer.com.
Kelly H
KH
Zou
Pfizer Inc., New York, NY, USA. Electronic address: Ching-Ray.Yu@pfizer.com.
Ching-Ray
CR
Yu
Department of Statistics, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA. Electronic address: LiuKeZhen@gmail.com.
Kezhen
K
Liu
Pfizer Inc., New York, NY, USA. Electronic address: Franklin.Sun@pfizer.com.
Franklin W
FW
Sun
D015982:Bias; D001699:Biometry; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design; D018709:Statistics, Nonparametric
Analysis of covariance; Covariate imbalance; Percent change from baseline; Randomized controlled trials; Robust regression