Bristol D R
Schering-Plough Research Institute, Kenilworth, New Jersey 07033, USA.
J Biopharm Stat. 1997 May;7(2):313-21; discussion 323-31. doi: 10.1080/10543409708835189.
The analysis of data from clinical trials often includes subgroup analyses, which are performed to examine the treatment effect within various sets of patients based on baseline and/or demographic variables. The goals of these analyses are to establish the consistency of the results across the subgroups and to identify important prognostic factors. The p-values for such analyses are usually presented without any adjustment for the multiple analyses: This approach has been criticized because of the possibility of misleading false positives. Conservative approaches have been proposed to resolve this problem; however, these approaches are usually so conservative that significant results are rarely observed after adjustment. Here an approximate technique for use when the variable of interest has a normal distribution is presented.
对临床试验数据的分析通常包括亚组分析,其目的是根据基线和/或人口统计学变量,在不同患者组中检验治疗效果。这些分析的目标是确定各亚组结果的一致性,并识别重要的预后因素。此类分析的p值通常在未对多重分析进行任何校正的情况下给出:这种方法受到了批评,因为存在产生误导性假阳性结果的可能性。有人提出了保守的方法来解决这个问题;然而,这些方法通常过于保守,以至于校正后很少能观察到显著结果。本文介绍一种当感兴趣的变量呈正态分布时使用的近似技术。