对基因意义的进一步质疑

Jennifer Nguyen, Daniel Hu
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引用次数: 3

摘要

最近在《头痛》杂志上发表的一篇题为《皇帝的新基因:新的口服CGRP拮抗剂的效果有临床意义吗?》在Tfelt-Hansen和Loder的研究中,对巨量妊娠和巨量妊娠试验的统计和临床意义提出了质疑——这是理所当然的。在他们的信中,作者评论说,虽然这些基因在试验中取得了统计学意义,但它们的临床效果可能是边际的。然而,这些试验的统计意义可能值得重新审视:除了使用治疗增益和治疗所需的数量外,计算脆弱性指数(FI)可以通过补充P值进一步帮助评估统计意义。而P值<。05是目前表示统计显著性的标准,它可能过于简单化而忽略了其他可能影响显著性的因素。在一组中,只有几个事件的变化可能使统计上显著的结果变为不显著的结果,反之亦然。考虑两项具有统计学意义的试验,其中随机患者的数量相差10倍。在较小的试验中,统计显著性可能只取决于少数事件,没有这些事件,P值可能会转移到不显著性- P值在表面上无法传达这种限制。因此,FI的效用是确定实现这种转变所需的事件数量。确定失去随访的患者数量并与FI进行直接比较也很重要;即,如果失去随访的患者数量超过FI,临床医生可能会质疑是否包括一定数量的结果可能导致统计显著性的变化。如果FI小于失去随访的患者数量,则认为结果具有统计学意义,则应谨慎解释。表1中一些超大和超大试验的FI值有助于评估这些试验的统计显著性。以ACHIEVE I试验为例:当比较50 mg增肥组和安慰剂组时,FI为11,失去随访的患者数量为45。如果没有随访的患者的数据也包括在内,试验的结果可能会从统计上显著转变为不显著。另一个例子是试验301,它调查了rimegepant:它的第一个主要终点的FI为2,这意味着该研究的统计显著性取决于N = 1084的研究中的2名参与者。最初研究者随机选取了1162名受试者,这可能表明失访患者的数量可能超过FI。通过补充P值,报告FI可以帮助临床医生在考虑统计和临床意义时得出适当的结论。
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Further Questioning of the Significance of the Gepants
In a recent paper in Headache, titled “The Emperor’s New Gepants: Are the Effects of the New Oral CGRP Antagonists Clinically Meaningful?” by Tfelt-Hansen and Loder, the statistical and clinical significances of the trials for rimegepant and ubrogepant were called into question – and rightfully so. In their letter, the authors remark that while the gepants have achieved statistical significance in trials, their clinical effect may be marginal. However, the statistical significance of these trials may be worth a second look: In addition to using therapeutic gain and number needed to treat, calculating a fragility index (FI) can further assist in assessing statistical significance by complementing the P value. While a P value of <.05 is the current standard for indicating statistical significance, it may be too simplistic and ignore other factors that can affect significance. A shift of only a few events in 1 group could shift the statistically significant results to nonsignificant – or vice versa. Consider 2 statistically significant trials in which the number of randomized patients differs significantly by a factor of 10. In the smaller trial, statistical significance may hinge on only a few events, without which the P value could be shifted to nonsignificance – the P value when taken at face value is unable to communicate that limitation. The utility of the FI therefore is to identify the number of events required to make that shift. It is also important to identify the number of patients lost to follow up and make a direct comparison to the FI; ie, if the number of patients lost to follow up exceeds the FI, the clinician may question if including a certain number of outcomes could have resulted in a shift in statistical significance. Results that are deemed statistically significant with FI less than the number of patients lost to follow up should be interpreted with caution. The FI’s for some of the rimegepant and ubrogepant trials in Table 1 can help when evaluating the statistical significance of these trials. Take for example the ACHIEVE I trial: when comparing the ubrogepant 50 mg group and placebo, the FI is 11 and the number of patients lost to follow up is 45. Had the data for the patients lost to follow up been included, the results of the trial could have shifted from statistically significant to non-significant. Another example is trial 301 which investigated rimegepant: its FI is 2 for its first primary endpoint, meaning that the statistical significance of this study hinged on just 2 participants in a study with N = 1084. Originally the investigators had randomized 1162 subjects, which may indicate that the number of patients lost to follow up may exceed the FI. Reporting the FI can assist clinicians in drawing the appropriate conclusions when considering statistical and clinical significance by complementing the P value.
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