The Romano-Wolf multiple-hypothesis correction in Stata
The Romano-Wolf multiple-hypothesis correction in Stata
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DOI：
10.1177/1536867x20976314
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发表时间：
2020-12-01
期刊：
影响因子：
4.8
通讯作者：
Wolf, Michael
中科院分区：
文献类型：
--
作者：
Clarke, Damian;Romano, Joseph P.;Wolf, Michael
当同时考虑多个假设检验时，除非明确考虑检验框架的多重性，否则标准统计技术将导致零假设的过度拒绝。在本文中，我们讨论了Romano-Wolf多重假设校正，并记录了其在Stata中的实现。Romano-Wolf修正（渐近地）控制家族错误率，即在被检验的假设家族中拒绝至少一个真实零假设的概率。这种校正比早期的多重测试程序（如Bonferroni和Holm校正）要强大得多，因为它通过从原始数据重新采样来考虑测试统计的依赖结构。我们描述了一个命令rwolf，它实现了这种修正，并提供了基于各种模型的几个示例。我们记录并讨论了使用rwolf相比其他控制家族错误率的多重测试过程的性能提升。
When considering multiple-hypothesis tests simultaneously, standard statistical techniques will lead to overrejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this article, we discuss the Romano-Wolf multiple-hypothesis correction and document its implementation in Stata. The Romano-Wolf correction (asymptotically) controls the familywise error rate, that is, the probability of rejecting at least one true null hypothesis among a family of hypotheses under test. This correction is considerably more powerful than earlier multiple-testing procedures, such as the Bonferroni and Holm corrections, given that it takes into account the dependence structure of the test statistics by resampling from the original data. We describe a command, rwolf, that implements this correction and provide several examples based on a wide range of models. We document and discuss the performance gains from using rwolf over other multiple-testing procedures that control the familywise error rate.