Leon Andrew C, Heo Moonseong
Weill Cornell Medical College, Department of Psychiatry, New York 10021, USA.
J Biopharm Stat. 2005;15(5):839-55. doi: 10.1081/BIP-200067922.
Several Bonferroni-type adjustments have been proposed to control for family wise type I error among multiple tests. However, many of the approaches disregard the correlation among endpoints. This can result in a conservative hypothesis testing strategy. The James procedure is an alternative approach that accounts for multiplicity among correlated continuous endpoints. Here a simulation study compares four Bonferroni-type alpha-adjustments (Bonferroni, Dunn-Sidák, Holm, and Hochberg) and the James p-value adjustment when used for multiple correlated binary variables. These procedures provided adequate protection against familywise type I error for correlated binary endpoints, albeit, at times, in an overly cautious manner. That is, when correlations among endpoints exceed 0.60, the result is somewhat conservative for the approaches that do not account for those correlations. Among the adjustments examined, the James approach appears to be the uniformly preferred method. Analyses of data from a randomized controlled clinical trial of treatments for mania in bipolar disorder are used to illustrate the application of the multiplicity adjustments.
已经提出了几种邦费罗尼（Bonferroni）类型的调整方法来控制多重检验中的家族性 I 型错误。然而，许多方法都忽略了终点之间的相关性。这可能导致一种保守的假设检验策略。詹姆斯（James）方法是一种替代方法，它考虑了相关连续终点之间的多重性。这里的一项模拟研究比较了四种邦费罗尼类型的α调整方法（邦费罗尼、邓恩-西达克（Dunn-Sidák）、霍尔姆（Holm）和霍赫贝格（Hochberg））以及用于多个相关二元变量时的詹姆斯 p 值调整方法。这些方法为相关二元终点提供了足够的保护，以防止家族性 I 型错误，尽管有时方式过于谨慎。也就是说，当终点之间的相关性超过0.60时，对于那些不考虑这些相关性的方法，结果会有些保守。在所研究的调整方法中，詹姆斯方法似乎是一致首选的方法。对双相情感障碍躁狂症治疗的一项随机对照临床试验的数据进行分析，以说明多重性调整的应用。
