Further Questioning of the Significance of the Gepants: A Response

E. Loder, P. Tfelt-Hansen
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Abstract

We thank Drs. Nguyen and Hu for their comments on our paper, and for suggesting the fragility index as a method to assess claims about statistical significance. Its major virtue is to draw attention to the number of events that would have to change in the control group in order to shift the P value to above .05. In a large series of randomized, controlled trials (RCTs), the median fragility index was 8, and such low numbers may help to identify less robust results. The calculation of fragility indices can probably be useful in methodological evaluation of RCTs, but it is most likely not suitable for evaluation of the clinical relevance of the results in an RCT. For this purpose, the calculation of therapeutic gain with 95% CI conveys more clinically relevant information. To us and to over 800 signatories of a recent editorial in the journal Nature, however, the larger problem seems to be the “dichotomania” that prevails in interpreting P values. Any P value threshold is artificial. It is naive and simplistic to use P values to claim that effects are present or absent. Instead, P values should be interpreted as a continuous measure and study findings should be framed in terms of clinical benefit. Researchers and readers should be encouraged to consider whether, across all values within the 95% confidence interval, there is evidence of meaningful medical effects. It can be difficult to decide whether trial findings are clinically important, and such determinations are often context-specific. No metric solves all problems of interpretation or can substitute for common sense and clinical judgment. Everyone should beware of claims that study findings are “highly statistically significant.” The next time someone invites you to admire a very tiny P value, consider that they may be hoping you will “pay no attention to that [equally tiny effect size] behind the curtain.”
对基因意义的进一步质疑:回应
我们感谢dr。Nguyen和Hu对我们论文的评论,以及他们建议将脆弱性指数作为评估统计显著性主张的方法。它的主要优点是让人们注意到,为了将P值移动到0.05以上,在对照组中必须改变的事件数量。在大量随机对照试验(rct)中,脆弱性指数的中位数为8,如此低的数字可能有助于识别不太可靠的结果。脆弱性指数的计算可能对随机对照试验的方法学评价有用,但它很可能不适合评估随机对照试验结果的临床相关性。为此,以95% CI计算治疗增益传达了更多的临床相关信息。然而,对我们和《自然》杂志最近一篇社论的800多位签名者来说,更大的问题似乎是在解释P值时普遍存在的“二分法”。任何P值阈值都是人为的。使用P值来断言效果存在或不存在是天真和简单的。相反,P值应该被解释为一个连续的测量,研究结果应该根据临床获益来构建。应鼓励研究人员和读者考虑,在95%置信区间内的所有值中,是否存在有意义的医学效果的证据。很难确定试验结果是否具有临床重要性,而且这种决定通常是根据具体情况而定的。没有一个度量标准能解决所有的解释问题,也不能代替常识和临床判断。每个人都应该警惕那些声称研究结果具有“高度统计意义”的说法。下次有人邀请你欣赏一个非常小的P值时,考虑一下他们可能希望你“不注意幕后的(同样小的效应值)”。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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