相依条件下广义Fisher组合检验$p$值的精确计算
Accurate $p$-Value Calculation for Generalized Fisher's Combination Tests Under Dependence
摘要：
组合显著性的相关检验具有广泛的应用，但$p$值计算具有挑战性。目前的力矩匹配方法（例如，Brown近似值）在小于0.05的水平上倾向于显著地增大第一类错误率。它可能会导致大数据分析中的重大错误发现。本文提供了几个更准确和计算效率更高的$p$值计算方法的一般家庭的Fisher型统计量，简称为GFisher。GFisher涵盖Fisher组合、Good统计、Lancaster统计、加权Z分数组合等。它允许灵活的加权方案，以及自动适应给定数据的适当权重和自由度的综合程序。新的$p$值计算方法是基于矩比匹配和联合分布替代的新思想。系统的模拟表明，他们是准确的多元高斯下，和鲁棒的广义线性模型和多元$t$ -分布下，至少下降到$10^{-6}$水平。我们说明了有用的GFisher和新的$p$值计算方法在分析模拟和真实数据的基因为基础的SNP-集关联研究遗传学。相关计算已在R软件包$GFisher$中实现。
英文摘要：
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 $GFisher$.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
