The harmonic mean p-value for combining dependent tests
The harmonic mean p-value for combining dependent tests
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DOI：
10.1073/pnas.1814092116
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发表时间：
2019-01-22
影响因子：
11.1
通讯作者：
Wilson, Daniel J.
中科院分区：
文献类型：
--
作者：
Wilson, Daniel J.
对“大数据”的分析经常涉及对数百万个相互竞争的假设进行统计比较，以发现所观察到的数据模式背后的隐藏过程，例如，在全基因组关联研究（GWAS）中寻找疾病的遗传决定因素。控制家族误差率（FWER）被认为是防止假阳性的最强保护，但很难达到多重检验校正的显著性阈值。在这里，我引入调和平均p值（HMP），它控制了FWER，同时通过使用广义中心极限定理结合相关检验大大提高了统计能力。我表明，HMP毫不费力地结合信息，在个体不显著的假设组中检测统计上显著的信号，例如人类神经过敏的GWAS和丙型肝炎病毒载量的联合人类病原体GWAS。HMP同时测试所有分组假设的方法，允许寻找最小的保留意义的假设组。HMP检测重要假设组的能力大于benjamin - hochberg程序检测重要假设的能力，尽管后者仅控制较弱的错误发现率（FDR）。HMP对大型数据集的分析具有广泛的意义，因为它增强了科学发现的潜力。
Analysis of "big data" frequently involves statistical comparison of millions of competing hypotheses to discover hidden processes underlying observed patterns of data, for example, in the search for genetic determinants of disease in genome-wide association studies (GWAS). Controlling the familywise error rate (FWER) is considered the strongest protection against false positives but makes it difficult to reach the multiple testing-corrected significance threshold. Here, I introduce the harmonic mean p-value (HMP), which controls the FWER while greatly improving statistical power by combining dependent tests using generalized central limit theorem. I show that the HMP effortlessly combines information to detect statistically significant signals among groups of individually nonsignificant hypotheses in examples of a human GWAS for neuroticism and a joint human-pathogen GWAS for hepatitis C viral load. The HMP simultaneously tests all ways to group hypotheses, allowing the smallest groups of hypotheses that retain significance to be sought. The power of the HMP to detect significant hypothesis groups is greater than the power of the Benjamini-Hochberg procedure to detect significant hypotheses, although the latter only controls the weaker false discovery rate (FDR). The HMP has broad implications for the analysis of large datasets, because it enhances the potential for scientific discovery.