Kelter Riko
Department of Mathematics, University of Siegen, Germany.
Stat Methods Med Res. 2023 Oct;32(10):1880-1901. doi: 10.1177/09622802231184636. Epub 2023 Jul 31.
The success of preclinical research hinges on exploratory and confirmatory animal studies. Traditional null hypothesis significance testing is a common approach to eliminate the chaff from a collection of drugs, so that only the most promising treatments are funneled through to clinical research phases. Balancing the number of false discoveries and false omissions is an important aspect to consider during this process. In this paper, we compare several preclinical research pipelines, either based on null hypothesis significance testing or based on Bayesian statistical decision criteria. We build on a recently published large-scale meta-analysis of reported effect sizes in preclinical animal research and elicit a non-informative prior distribution under which both approaches are compared. After correcting for publication bias and shrinkage of effect sizes in replication studies, simulations show that (i) a shift towards statistical approaches which explicitly incorporate the minimum clinically important difference reduces the false discovery rate of frequentist approaches and (ii) a shift towards Bayesian statistical decision criteria can improve the reliability of preclinical animal research by reducing the number of false-positive findings. It is shown that these benefits hold while keeping the number of experimental units low which are required for a confirmatory follow-up study. Results show that Bayesian statistical decision criteria can help in improving the reliability of preclinical animal research and should be considered more frequently in practice.
临床前研究的成功取决于探索性和确证性动物研究。传统的零假设显著性检验是从一系列药物中筛选出有效药物的常用方法，这样只有最有前景的治疗方法才能进入临床研究阶段。在此过程中，平衡假阳性发现和假阴性遗漏的数量是一个需要考虑的重要方面。在本文中，我们比较了几种临床前研究流程，这些流程要么基于零假设显著性检验，要么基于贝叶斯统计决策标准。我们基于最近发表的一项对临床前动物研究中报告的效应量的大规模荟萃分析，并引出一种无信息先验分布，在此分布下对两种方法进行比较。在对复制研究中的发表偏倚和效应量收缩进行校正后，模拟结果表明：（i）转向明确纳入最小临床重要差异的统计方法可降低频率主义方法的假阳性发现率；（ii）转向贝叶斯统计决策标准可通过减少假阳性发现的数量来提高临床前动物研究的可靠性。结果表明，在确证性后续研究所需的实验单元数量较少的情况下，这些益处依然存在。结果表明，贝叶斯统计决策标准有助于提高临床前动物研究的可靠性，在实践中应更频繁地予以考虑。
