ADAMA Deutschland GmbH, Edmund-Rumpler-Strasse 6, 51149, Cologne, Germany.
, Lauenau, Germany.
Arch Toxicol. 2020 Apr;94(4):1135-1149. doi: 10.1007/s00204-020-02690-w. Epub 2020 Mar 19.
The goal of (eco-) toxicological testing is to experimentally establish a dose or concentration-response and to identify a threshold with a biologically relevant and probably non-random deviation from "normal". Statistical tests aid this process. Most statistical tests have distributional assumptions that need to be satisfied for reliable performance. Therefore, most statistical analyses used in (eco-)toxicological bioassays use subsequent pre- or assumption-tests to identify the most appropriate main test, so-called statistical decision trees. There are however several deficiencies with the approach, based on study design, type of tests used and subsequent statistical testing in general. When multiple comparisons are used to identify a non-random change against negative control, we propose to use robust testing, which can be generically applied without the need of decision trees. Visualization techniques and reference ranges also offer advantages over the current pre-testing approaches. We aim to promulgate the concepts in the (eco-) toxicological community and initiate a discussion for regulatory acceptance.
（生态）毒理学测试的目的是通过实验建立剂量-反应关系，并确定一个具有生物学相关性且可能非随机的与“正常”情况偏离的阈值。统计检验有助于这一过程。大多数统计检验都有分布假设，需要满足这些假设才能可靠地进行。因此，（生态）毒理学生物测定中使用的大多数统计分析都使用后续的预检验或假设检验来确定最合适的主要检验，即所谓的统计决策树。然而，基于研究设计、使用的测试类型以及一般的后续统计测试，这种方法存在几个缺陷。当使用多个比较来识别与阴性对照的非随机变化时，我们建议使用稳健检验，无需决策树即可通用应用。可视化技术和参考范围也比当前的预测试方法具有优势。我们旨在向（生态）毒理学界宣传这些概念，并发起关于监管接受的讨论。
