Discovering Findings That Replicate From a Primary Study of High Dimension to a Follow-Up Study
Discovering Findings That Replicate From a Primary Study of High Dimension to a Follow-Up Study
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DOI：
10.1080/01621459.2013.829002
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发表时间：
2013-12-01
影响因子：
3.7
通讯作者：
Heller, Ruth
中科院分区：
文献类型：
--
作者：
Bogomolov, Marina;Heller, Ruth
我们考虑的问题，确定是否复制的结果从一个研究的高维度到另一个，当主要的研究指导选择的假设进行检查，在后续的研究，以及当没有分工的主要和后续的研究。我们表明，现有的荟萃分析方法是不适合这个问题，并提出新的方法，而不是。我们证明，我们的多个测试程序控制适当的错误率。建议的家庭明智的错误率控制程序是有效的任意依赖之间的检验统计量在每个研究。一个更强大的程序建议错误发现率（FDR）的控制。我们证明，如果检验统计量在初步研究中独立，并且在后续研究中独立或具有正相关性，则该程序可以控制FDR。对于主要研究中的任意依赖性，以及随访研究中的任意依赖性或正依赖性，对程序进行简单的保守修改控制FDR。我们通过模拟和真实的数据例子证明了这些程序的有用性。本文的补充材料可在网上查阅。
We consider the problem of identifying whether findings replicate from one study of high dimension to another, when the primary study guides the selection of hypotheses to be examined in the follow-up study as well as when there is no division of roles into the primary and the follow-up study. We show that existing meta-analysis methods are not appropriate for this problem, and suggest novel methods instead. We prove that our multiple testing procedures control for appropriate error rates. The suggested family-wise error rate controlling procedure is valid for arbitrary dependence among the test statistics within each study. A more powerful procedure is suggested for false discovery rate (FDR) control. We prove that this procedure controls the FDR if the test statistics are independent within the primary study, and independent or have positive dependence in the follow-up study. For arbitrary dependence within the primary study, and either arbitrary dependence or positive dependence in the follow-up study, simple conservative modifications of the procedure control the FDR. We demonstrate the usefulness of these procedures via simulations and real data examples. Supplementary materials for this article are available online.