对具有许多协变量的数据集中的总体原假设进行综合排列检验。

Omnibus permutation tests of the overall null hypothesis in datasets with many covariates.

作者信息

Potter Douglas M, Griffiths Derek J

机构信息

Biostatistics Department, Graduate School of Public Health, University of Pittsburgh and Biostatistics Facility, University of Pittsburgh Cancer Institute, PA 15213, USA.

出版信息

J Biopharm Stat. 2006 May;16(3):327-41. doi: 10.1080/10543400600609585.

DOI:10.1080/10543400600609585

PMID:16724488

Abstract

Tests of the overall null hypothesis in datasets with one outcome variable and many covariates can be based on various methods to combine the p-values for univariate tests of association of each covariate with the outcome. The overall p-value is computed by permuting the outcome variable. We discuss the situations in which this approach is useful and provide several examples. We use simulations to investigate seven omnibus test statistics and find that the Anderson-Darling and Fisher's statistics are superior to the others.

摘要

在具有一个结果变量和多个协变量的数据集中，对总体零假设的检验可以基于各种方法来合并每个协变量与结果的单变量关联检验的p值。总体p值通过对结果变量进行置换来计算。我们讨论了这种方法有用的情况并提供了几个例子。我们使用模拟来研究七种综合检验统计量，发现安德森-达林统计量和费舍尔统计量优于其他统计量。