On the adaptive control of the false discovery fate in multiple testing with independent statistics
On the adaptive control of the false discovery fate in multiple testing with independent statistics
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DOI：
10.2307/1165312
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发表时间：
2000-03-01
影响因子：
2.4
通讯作者：
Hochberg, Y
中科院分区：
文献类型：
--
作者：
Benjamini, Y;Hochberg, Y
Benjamini和Hochberg（1995）提出了一种解决多重显著性检验问题的新方法，该方法要求控制错误拒绝数与拒绝数的期望比值--错误发现率（FDR）。所给出的程序被证明可以控制独立检验统计量的FDR。当某些假设事实上是错误的时候，这种方法就太保守了。Ne在这里提出了一个自适应过程，其中如Hochberg和Benjamini（1990）中那样首先估计真零假设的数量，并且该估计用于Benjamini和Hochberg（1995）的过程。结果仍然是一个简单的逐步过程，我们也给了一个图形伴侣。新的程序被用于从教育和行为研究中得出的几个例子，解决多中心研究，子集分析和荟萃分析中的问题。这些例子在测试的假设数量以及新程序对结论的影响方面有所不同。III独立的测试统计量的大规模模拟研究的自适应程序被示出控制FDR，并具有实质上更好的功率比先前建议的FDR控制方法，其本身是更强大的比传统的familywise错误率控制方法。在大多数被检验的假设远不为真的情况下，由于同时检验许多假设，几乎没有任何惩罚。
A new approach to problems of multiple significance testing was presented in Benjamini and Hochberg (1995), which calls for controlling the expected ratio of the number of erroneous rejections to the number of rejections-the False Discovery Rate (FDR). The procedure given there was shown to control the FDR for independent test statistics. When some of the hypotheses are in fact false, that procedure is too conservative. Ne present here an adaptive procedure, where the number of true null hypotheses is estimated first as in Hochberg and Benjamini (1990), and this estimate is used in the procedure of Benjamini and Hochberg (1995). The result is still a simple stepwise procedure, to which we also give a graphical companion. The new procedure is used in several examples drawn from educational and behavioral studies, addressing problems in multi-center studies, subset analysis and meta-analysis. The examples vary in the number of hypotheses tested, and the implication of the new procedure on the conclusions. III a large simulation study of independent test statistics the adaptive procedure is shown to control the FDR and have substantially better power than the previously suggested FDR controlling method, which by itself is more powerful than the traditional familywise error-rate controlling methods. In cases where most of the tested hypotheses are far from being true there is hardly any penalty due to the simultaneous testing of many hypotheses.