Online control of the false discovery rate in biomedical research
Online control of the false discovery rate in biomedical research
复制标题
生物医学研究中错误发现率的在线控制
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Wason
中科院分区：
文献类型：
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作者：
D. Robertson;J. Wason
现代生物医学研究经常涉及测试多个相关假设，同时保持对适当错误率的控制。在许多应用中，错误发现率（FDR），即被拒绝的假设中假阳性的预期比例，已成为标准误差标准。控制FDR的程序，例如众所周知的Benjamini-Hochberg程序，假设所有p值均可在单个时间点进行检验。然而，这忽略了许多生物医学实验的连续性，在这些实验中，一系列假设被测试，而无法获得未来的p值甚至假设的数量。最近，Javanmard和Montanari（Ann. Stat. 2018），并由Ramdas等人建立（NIPS 2017，ICML 2018）。在本文中，我们比较和对比这些建议的程序，特别侧重于设置的p值是依赖的。我们还提出了一个简单的修改程序时，有一个上限的假设进行测试的数量。使用全面的模拟场景和案例研究，我们提供了在实践中使用哪些程序进行在线FDR控制的建议。
Modern biomedical research frequently involves testing multiple related hypotheses, while maintaining control over a suitable error rate. In many applications the false discovery rate (FDR), which is the expected proportion of false positives among the rejected hypotheses, has become the standard error criterion. Procedures that control the FDR, such as the well-known Benjamini-Hochberg procedure, assume that all p-values are available to be tested at a single time point. However, this ignores the sequential nature of many biomedical experiments, where a sequence of hypotheses is tested without having access to future p-values or even the number of hypotheses. Recently, the first procedures that control the FDR in this online manner have been proposed by Javanmard and Montanari (Ann. Stat. 2018), and built upon by Ramdas et al. (NIPS 2017, ICML 2018). In this paper, we compare and contrast these proposed procedures, with a particular focus on the setting where the p-values are dependent. We also propose a simple modification of the procedures for when there is an upper bound on the number of hypotheses to be tested. Using comprehensive simulation scenarios and case studies, we provide recommendations for which procedures to use in practice for online FDR control.
影响因子：
3.7
作者：
Groppe DM;Urbach TP;Kutas M
通讯作者：
Kutas M