Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA.
Department of Cardiothoracic Surgery, Weill Cornell Medicine, New York, NY, USA.
J Clin Epidemiol. 2021 Nov;139:199-209. doi: 10.1016/j.jclinepi.2021.08.010. Epub 2021 Aug 15.
The fragility index is a clinically interpretable metric increasingly used to interpret the robustness of clinical trials results that is generally not incorporated in sample size calculation and applied post-hoc. In this manuscript, we propose to base the sample size calculation on the fragility index in a way that supplements the classical prefixed alpha and power cutoffs and we provide a dedicated R software package for the design and analysis tools.
This approach follows from a novel hypothesis testing framework that is based on the fragility index and builds on the classical testing approach. As case studies, we re-analyse the design of two important trials in cardiovascular medicine, the FAME and FAMOUS-NSTEMI trials.
The analyses show that approach returns sample sizes which results in a higher power for the P value based test and most importantly a lower and context dependent Type I error rate for the fragility index based test compared to standard tests.
Our method allows clinicians to control for the fragility index during clinical trial design.
脆弱指数是一种临床上可解释的指标，越来越多地用于解释临床试验结果的稳健性，通常不在样本量计算中纳入，也不在事后应用。在本文中，我们提议基于脆弱指数进行样本量计算，以此补充经典的预设α和功效截止值，并提供一个专用的 R 软件包用于设计和分析工具。
这种方法源于基于脆弱指数的新假设检验框架，并建立在经典检验方法的基础上。作为案例研究，我们重新分析了心血管医学中两项重要试验（FAME 和 FAMOUS-NSTEMI 试验）的设计。
分析表明，该方法返回的样本量可提高基于 P 值的检验功效，并且与标准检验相比，基于脆弱指数的检验的Ⅰ类错误率更低且与背景相关。
我们的方法允许临床医生在临床试验设计中控制脆弱指数。
