Cupples L A, Heeren T, Schatzkin A, Colton T
Ann Intern Med. 1984 Jan;100(1):122-9. doi: 10.7326/0003-4819-100-1-122.
Researchers frequently encounter studies that compare two groups on many variables. We discourage the use of multiple tests of hypotheses on individual variables, an approach that ignores the correlation among the variables and increases the chance of a type I error. Instead of examining each variable separately, we recommend using multivariate procedures that integrate all measures on a person into a unified analysis of the differences between the two groups. We describe three multivariate procedures: Hotelling's T2, discriminant analysis, and logistic regression. We also discuss the use of Bonferroni's adjustment to preserve the overall chance of a type I error in conducting individual tests on each variable after doing the multivariate procedures. We review the underlying assumptions and relative merits and disadvantages of the three multivariate methods and recommend which method to use in various circumstances.
研究人员经常会遇到在多个变量上比较两组的研究。我们不鼓励对单个变量进行多次假设检验，这种方法忽略了变量之间的相关性，并增加了I型错误的可能性。我们建议不要分别检查每个变量，而是使用多变量程序，将一个人的所有测量值整合到对两组之间差异的统一分析中。我们描述了三种多变量程序：霍特林T2检验、判别分析和逻辑回归。我们还讨论了在进行多变量程序后，使用邦费罗尼校正来保持对每个变量进行单独检验时I型错误的总体概率。我们回顾了这三种多变量方法的基本假设以及相对优缺点，并推荐在各种情况下使用哪种方法。
