比较非参数和参数方法来调整基线测量。

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

机构信息

Pfizer Inc., New York, NY, USA; Department of Statistics, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.

Pfizer Inc., New York, NY, USA.

出版信息

Contemp Clin Trials. 2014 Mar;37(2):225-33. doi: 10.1016/j.cct.2014.01.002. Epub 2014 Jan 21.

DOI:10.1016/j.cct.2014.01.002

PMID:24462567

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.

摘要

在分析随机对照试验时，我们可以使用各种统计方法来调整基线测量值。根据选择的调整基线测量值的方法，推断结果可能会有所不同。我们研究了基于参数和非参数方法比较治疗结果的检验的Ⅰ类错误和统计功效。我们还探讨了在存在或不存在潜在正态性的情况下，基线与从基线变化之间的相关性水平不断增加的情况。通过模拟来演示和比较这些方法。