Radiology Unit, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Italy.
Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Morandi 30, 20097, San Donato Milanese, Italy.
Eur Radiol Exp. 2020 Mar 11;4(1):18. doi: 10.1186/s41747-020-0145-y.
Here, we summarise the unresolved debate about p value and its dichotomisation. We present the statement of the American Statistical Association against the misuse of statistical significance as well as the proposals to abandon the use of p value and to reduce the significance threshold from 0.05 to 0.005. We highlight reasons for a conservative approach, as clinical research needs dichotomic answers to guide decision-making, in particular in the case of diagnostic imaging and interventional radiology. With a reduced p value threshold, the cost of research could increase while spontaneous research could be reduced. Secondary evidence from systematic reviews/meta-analyses, data sharing, and cost-effective analyses are better ways to mitigate the false discovery rate and lack of reproducibility associated with the use of the 0.05 threshold. Importantly, when reporting p values, authors should always provide the actual value, not only statements of "p < 0.05" or "p ≥ 0.05", because p values give a measure of the degree of data compatibility with the null hypothesis. Notably, radiomics and big data, fuelled by the application of artificial intelligence, involve hundreds/thousands of tested features similarly to other "omics" such as genomics, where a reduction in the significance threshold, based on well-known corrections for multiple testing, has been already adopted.
在这里，我们总结了关于 p 值及其二分法的未解决争议。我们介绍了美国统计协会反对滥用统计显著性的声明，以及放弃使用 p 值和将显著性阈值从 0.05 降低到 0.005 的建议。我们强调了采取保守方法的原因，因为临床研究需要二分法答案来指导决策，特别是在诊断成像和介入放射学的情况下。随着 p 值阈值的降低，研究成本可能会增加，而自发研究可能会减少。来自系统评价/荟萃分析、数据共享和成本效益分析的二级证据是减轻与使用 0.05 阈值相关的假发现率和可重复性缺乏的更好方法。重要的是，当报告 p 值时，作者应始终提供实际值，而不仅仅是“p < 0.05”或“p ≥ 0.05”的声明，因为 p 值衡量数据与零假设的兼容性程度。值得注意的是，放射组学和大数据，由人工智能的应用推动，涉及数百/数千个经过测试的特征，类似于其他“组学”，如基因组学，其中已经采用了基于已知多重检验校正的显著性阈值降低。
