Leibniz University Hannover, Working Group Biostatistics, D-30167, Hannover, Germany.
German Federal Institute for Risk Assessment (BfR), Department of Chemicals and Product Safety, Max-Dohrn-Straße 8-10, 10589 Berlin, Germany.
Regul Toxicol Pharmacol. 2020 Oct;116:104720. doi: 10.1016/j.yrtph.2020.104720. Epub 2020 Jul 6.
Recently it was recommended to avoid significance tests, in particular dichotomization into significant/non-significant on the basis of a p-value and a fixed 5% significance level (i.e. false positive rate). As an alternative, the interpretation of a suitable effect size and its compatibility interval is recommended, i.e. confidence intervals whose compatibility with the data, the assumptions, and the models is shown. This concept is used for the evaluation of assays in regulatory toxicology with special emphasis on the proof of hazard and proof of safety. Three case studies for multiple endpoints, multiple models and the consideration of historical controls illustrate the applicability of this concept. The corresponding software code for the open-source R project for statistical computing (www.r-project.org) is provided.
最近，有人建议避免使用显著性检验，特别是基于 p 值和固定的 5%显著性水平（即假阳性率）将数据二分为显著/不显著。作为替代方法，建议解释合适的效应大小及其置信区间，即显示与数据、假设和模型相兼容的置信区间。这个概念用于监管毒理学中分析方法的评估，特别强调危害证明和安全性证明。三个多终点、多模型和考虑历史对照的案例研究说明了这个概念的适用性。提供了用于统计计算的开源 R 项目（www.r-project.org）的相应软件代码。
