The Fragility Index has been introduced as a complement to the P‐value to summarize the statistical strength of evidence for a trial's result. The Fragility Index (FI) is defined in trials with two equal treatment group sizes, with a dichotomous or time‐to‐event outcome, and is calculated as the minimum number of conversions from nonevent to event in the treatment group needed to shift the P‐value from Fisher's exact test over the .05 threshold. As the index lacks a well‐defined probability motivation, its interpretation is challenging for consumers. We clarify what the FI may be capturing by separately considering two scenarios: (a) what the FI is capturing mathematically when the probability model is correct and (b) how well the FI captures violations of probability model assumptions. By calculating the posterior probability of a treatment effect, we show that when the probability model is correct, the FI inappropriately penalizes small trials for using fewer events than larger trials to achieve the same significance level. The analysis shows that for experiments conducted without bias, the FI promotes an incorrect intuition of probability, which has not been noted elsewhere and must be dispelled. We illustrate shortcomings of the FI's ability to quantify departures from model assumptions and contextualize the FI concept within current debate around the null hypothesis significance testing paradigm. Altogether, the FI creates more confusion than it resolves and does not promote statistical thinking. We recommend against its use. Instead, sensitivity analyses are recommended to quantify and communicate robustness of trial results.

中文翻译：

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消除脆弱性指数：统计推理的证明。

引入脆弱性指数作为对P值的补充，以总结试验结果证据的统计强度。脆弱性指数（FI）在具有两个相等治疗组大小的试验中定义，具有二分或事件发生前的结果，并计算为将P偏移所需的治疗组从非事件到事件的最小转换数费舍尔精确检验得出的值超过.05阈值。由于该指数缺乏明确的概率动机，因此其解释对消费者而言具有挑战性。我们通过分别考虑以下两种情况来阐明FI可能捕获的内容：（a）当概率模型正确时，FI在数学上捕获的内容；（b）FI捕获违反概率模型假设的程度如何。通过计算治疗效果的后验概率，我们表明，当概率模型正确时，FI会不恰当地惩罚小试验，因为使用小事件要比大试验少，从而达到相同的显着性水平。分析表明，对于没有偏见的实验，FI会促进错误的直觉，这在其他地方没有注意到，必须予以消除。在当前关于零假设重要性检验范式的辩论中，我们说明了金融机构量化偏离模型假设和将金融机构概念背景化的能力的缺点。总而言之，金融机构造成的混乱更多于其无法解决的问题，并且不会促进统计思维。我们建议不要使用它。相反，建议进行敏感性分析以量化和传达试验结果的可靠性。