Department of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bangalore, India.
J Clin Psychiatry. 2020 Mar 24;81(2):20f13334. doi: 10.4088/JCP.20f13334.
The fragility index (FI) has been recommended for use as an additional statistic when presenting the results of randomized controlled trials (RCTs). The FI in a completed RCT is the smallest number of subjects whose status needs to be changed, such as from nonresponder to responder, for a statistically significant finding to lose its statistical significance. A small FI suggests that a finding is fragile; a large FI suggests that the finding is robust. Whereas an FI value of 0-1 indicates extreme fragility, there is no cutoff to separate what is small and what is large for the FI. The FI is useful because it helps readers understand significant findings of an RCT in a different and more intuitive way. The FI has limitations. It can only be calculated in the context of an RCT, and only when binary outcomes are compared between 2 groups. It should not be calculated in nonrandomized studies, because it cannot be adjusted for the biasing effect of confounding variables, nor in time-to-event studies, because it cannot include the effect of time. Interpretation of the FI can be problematic when the number of subjects who drop out for unknown reasons is large. RCTs with small samples and RCTs in which the event of interest is rare tend to be fragile. However, the most important limitation of the FI is that it revolves around the much decried use of a statistical threshold (usually P < .05) for determining the significance of a study finding. At best, the FI complements the understanding of the results of an RCT with statistically significant findings for categorical outcomes. It should be used and interpreted in the context of other statistical information, including summary statistics, measures of effect size, and confidence intervals.
脆性指数（FI）已被推荐用于呈现随机对照试验（RCT）结果的附加统计量。在完成的 RCT 中，FI 是需要改变状态的受试者的最小数量，例如从不响应者变为响应者，以使统计学上显著的发现失去统计学意义。较小的 FI 表明发现是脆弱的；较大的 FI 表明发现是稳健的。虽然 FI 值为 0-1 表示极度脆弱，但没有截止值来区分 FI 的大小。FI 很有用，因为它帮助读者以不同的、更直观的方式理解 RCT 的显著发现。FI 有其局限性。它只能在 RCT 的背景下计算，并且只能在比较 2 组之间的二分类结果时计算。它不应该在非随机研究中计算，因为它不能调整混杂变量的偏倚效应，也不应该在时间事件研究中计算，因为它不能包含时间的影响。当由于未知原因退出的受试者数量较大时，对 FI 的解释可能会出现问题。样本量小的 RCT 和感兴趣事件罕见的 RCT 往往很脆弱。然而，FI 的最重要限制是它围绕着一个备受诟病的使用统计阈值（通常为 P <.05）来确定研究发现的显著性。在最好的情况下，FI 补充了对具有统计学意义的分类结果的 RCT 结果的理解。它应该在其他统计信息的背景下使用和解释，包括汇总统计、效应大小度量和置信区间。
