避免事件比率的样本量估计。

IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
David T Dunn, Oliver T Stirrup, David V Glidden
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引用次数: 0

摘要

背景:避免事件比率(AER)是最近发展起来的具有事件发生时间结果的非劣效性主动控制预防试验的估计。与传统的率比或率差相比,AER是基于两种治疗中每一种治疗避免的事件数量,而不是观察到的事件。AER需要假设背景事件发生率(与事实相反的安慰剂发生率)或对照治疗的与事实相反的有效性。我们开发并提出了将AER定义为主要估计的试验的样本量公式,并根据比率比与传统的95-95方法进行了比较。方法:我们根据控制组和实验组的预期事件数和所需的人年随访来表达样本量。样本量公式基于对数尺度上的Wald置信区间,假设主动处理和对照处理同样有效。使用AER,样本量取决于分析是基于反事实的安慰剂发生率还是基于反事实的治疗效果。对于这两种方法和95-95方法,样本量是背景事件率、对照处理的有效性、效果保存量(非劣效裕度)、推断非劣效性的置信限和证明非劣效性的所需统计能力的函数。结果:使用基于反事实安慰剂发生率的AER获得最小样本量。控制治疗效果值越高,优势越大。例如,与95-95方法相比,对于50%的治疗效果(取决于非劣效边际),它允许样本量减少2.6- 4.0倍,对于80%的治疗效果,它允许样本量减少7.7- 11.9倍。基于对照治疗效果的AER效率较低,但仍需要比95-95方法更小的样本量:50%治疗效果减少1.5至2.9倍,80%治疗效果减少2.3至6.4倍。样本量对非劣效度高度敏感:将效果保存量从50%增加到60%意味着样本量增加1.84倍;从60%增至70%,增长2.15倍;从70%到80%,增长了2.55倍。结论:在主动对照非劣效性试验中,使用AER作为主要估计值,除了具有重要的解释优势外,还允许进行更小、更具成本效益的研究。理想情况下,如果可行,AER应通过反事实安慰剂发生率得出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sample size estimation for the averted events ratio.

Background: The averted events ratio (AER) is a recently developed estimand for non-inferiority active-control prevention trials with a time-to-event outcome. In contrast to the traditional rate ratio or rate difference, the AER is based on the number of events averted by each of the two treatments rather than the observed events. The AER requires an assumption about either the background event rate (the counterfactual placebo incidence) or the counterfactual effectiveness of the control treatment. We develop and present sample size formulae for trials in which the AER is defined as the primary estimand, and draw comparisons with the conventional 95-95 method based on the rate ratio.

Methods: We express sample size in terms of the expected number of events and required person-years follow-up in the control and experimental arms. Sample size formulae were based on Wald confidence intervals on a logarithmic scale, assuming the active and control treatments to be equally effective. Using the AER, sample size depends on whether the analysis will be based on the counterfactual placebo incidence or the counterfactual treatment effectiveness. For both approaches, and the 95-95 method, sample size is a function of the background event rate, the effectiveness of the control treatment, the preservation-of-effect size (non-inferiority margin), the confidence limit for inferring non-inferiority, and the desired statistical power to demonstrate non-inferiority.

Results: The smallest sample size is obtained using the AER based on the counterfactual placebo incidence. The advantage is greater the higher the value of the control treatment effectiveness. For example, compared with the 95-95 method, it allows between a 2.6-fold and 4.0-fold reduction in sample size for 50% treatment effectiveness (depending of the non-inferiority margin), and between a 7.7-fold and 11.9-fold reduction for 80% treatment effectiveness. The AER based on the control treatment effectiveness is less efficient but still requires smaller sample sizes than the 95-95 method: between a 1.5-fold and 2.9-fold reduction for 50% treatment effectiveness, and between a 2.3-fold and 6.4-fold reduction for 80% treatment effectiveness. Sample size is highly sensitive to the non-inferiority margin: increasing the preservation-of-effect size from 50% to 60% implies a 1.84-fold increase in the sample size; from 60% to 70%, an increase of 2.15-fold; and from 70% to 80%, an increase of 2.55-fold.

Conclusion: As well as having important advantages of interpretation, using the AER as the primary estimand in active-control non-inferiority trials permits smaller and more cost-effective studies. Ideally, the AER should be derived via the counterfactual placebo incidence when this is practicable.

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来源期刊
Clinical Trials
Clinical Trials 医学-医学:研究与实验
CiteScore
4.10
自引率
3.70%
发文量
82
审稿时长
6-12 weeks
期刊介绍: Clinical Trials is dedicated to advancing knowledge on the design and conduct of clinical trials related research methodologies. Covering the design, conduct, analysis, synthesis and evaluation of key methodologies, the journal remains on the cusp of the latest topics, including ethics, regulation and policy impact.
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