Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach
Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach
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DOI：
10.1186/s41747-020-0145-y
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发表时间：
2020-03-11
影响因子：
3.8
通讯作者：
Sardanelli, Francesco
中科院分区：
文献类型：
--
作者：
Di Leo, Giovanni;Sardanelli, Francesco
在这里，我们总结一下关于p值及其二分法的尚未解决的争论。我们介绍了美国统计协会反对滥用统计意义的声明以及放弃使用p值并将显著性阈值从0.05%降至0.005的建议。我们强调了保守方法的原因，因为临床研究需要两种答案来指导决策，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。来自系统审查/荟萃分析、数据共享和成本效益分析的次要证据是减少与使用0.05阈值相关的错误发现率和缺乏重复性的更好方法。重要的是，在报告p值时，作者应该始终提供实际值，而不仅仅是“p<；0.05”或“p>；=0.05”的声明，因为p值给出了数据与零假设兼容程度的衡量标准。值得注意的是，在人工智能应用的推动下，放射组学和大数据涉及成百上千个测试特征，类似于基因组学等其他“组学”，在基因组学等其他“组学”中，已经采用了基于众所周知的多次测试校正的重要性阈值降低。
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.