Abstract

Recent literature has shown that statistically significant results are often not replicated because the “p-value < 0.05” publication rule results in a high false positive rate (FPR) or false discovery rate (FDR) in some scientific communities. While recommendations to address the phenomenon vary, many amount to incorporating additional study summary information, such as prior null hypothesis odds and/or effect sizes, in some way. This article demonstrates that a statistic called the local false discovery rate (lfdr), which incorporates this information, is a sufficient summary for addressing false positive rates. Specifically, it is shown that lfdr-values among published results are sufficient for estimating the community-wide FDR for any well-defined publication policy, and that lfdr-values are sufficient for defining policies for community-wide FDR control. It is also demonstrated that, though p-values can be useful for computing an lfdr, they alone are not sufficient for addressing the community-wide FDR. Data from the recent replication study are used to compare publication policies and illustrate the FDR estimator.

摘要

最近的文献表明，统计上显着的结果通常不会被复制，因为“ p值 < 0.05”的发布规则会导致一些科学界的高假阳性率 (FPR) 或错误发现率 (FDR)。虽然解决这一现象的建议各不相同，但许多建议以某种方式纳入额外的研究摘要信息，例如先前的零假设几率和/或效应大小。本文演示了包含此信息的称为本地错误发现率 ( lfdr ) 的统计数据是解决误报率的充分摘要。具体来说，表明lfdr已发布结果中的 - 值足以估计任何明确定义的发布政策的社区范围 FDR，并且lfdr -值足以定义社区范围 FDR 控制的政策。还表明，尽管p值可用于计算lfdr，但仅凭它们不足以解决社区范围的 FDR。最近复制研究的数据用于比较出版政策并说明 FDR 估计量。