论文标题
准确的$ p $ - 价值计算，用于依赖的通用Fisher组合测试
Accurate $p$-Value Calculation for Generalized Fisher's Combination Tests Under Dependence
论文作者
论文摘要
将依赖性测试结合起来具有广泛的应用，但是$ p $ - 价值计算具有挑战性。当前的矩匹配方法（例如，Fisher组合测试的Brown的近似值）往往会在低于0.05的水平上显着夸大I型错误率。它可能导致大数据分析中的重大错误发现。本文为Fisher型统计数据（称为Gfisher）提供了几种更准确和计算上有效的$ P $值计算方法。 Gfisher涵盖了Fisher的组合，Good的统计数据，兰开斯特的统计数据，加权Z分数组合等。它允许灵活的加权方案以及综合程序，该程序自动适应了适当的权重和自由度，以适应给定数据。新的$ p $ - 价值计算方法基于矩比匹配和联合分布代理的新思想。系统的模拟表明，在多元高斯下，它们是准确的，并且在广义线性模型和多元$ t $ - 分布下稳健，至少为$ 10^{ - 6} $ latver。我们说明了GFISHER和新的$ P $值计算方法在分析基于基因基于基因的SNP-Set关联研究中的模拟和真实数据中的有用性。相关计算已在r软件包$ gfisher $中实施。
Combining dependent tests of significance has broad applications but the $p$-value calculation is challenging. Current moment-matching methods (e.g., Brown's approximation) for Fisher's combination test tend to significantly inflate the type I error rate at the level less than 0.05. It could lead to significant false discoveries in big data analyses. This paper provides several more accurate and computationally efficient $p$-value calculation methods for a general family of Fisher type statistics, referred as the GFisher. The GFisher covers Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. It allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and degrees of freedom to a given data. The new $p$-value calculation methods are based on novel ideas of moment-ratio matching and joint-distribution surrogating. Systematic simulations show that they are accurate under multivariate Gaussian, and robust under the generalized linear model and the multivariate $t$-distribution, down to at least $10^{-6}$ level. We illustrate the usefulness of the GFisher and the new $p$-value calculation methods in analyzing both simulated and real data of gene-based SNP-set association studies in genetics. Relevant computation has been implemented into R package $GFisher$.
