背景:适应性临床试验越来越受欢迎,因为它们更加灵活,比传统的固定设计更有效率和道德。然而,尽管它们在评估COVID-19治疗方面的使用越来越多,但它们在重症监护试验中的使用仍然有限。更好地理解各种自适应设计的相对优势可能会增加它们的使用和解释。
方法:使用两项大型重症监护试验(ADRENAL。
结果:政府编号,NCT01448109。更新12-12-2017;NICE糖。
结果:政府编号,NCT00220987。更新于2009年1月29日),我们评估了三种频率论和两种贝叶斯自适应方法的性能。我们回顾性地重新分析了一项试验,两个,四,和九个等距间隔。使用最初的假设,我们进行了10,000次模拟以得出错误率,做出早期正确和错误决定的概率,零情景(无治疗效果)和替代情景(积极治疗效果)下的预期样本量和治疗效果估计。我们使用逻辑回归模型,以90天死亡率作为结果,以治疗组作为协变量。使用0.05的双侧显著性水平(α)检验零假设。
结果:在所有方法中,增加间隔的数量导致预期样本量减少。在null场景下,组顺序方法可以很好地控制I型错误率;然而,I型错误率通货膨胀是贝叶斯方法的一个问题。贝叶斯预测概率和O'Brien-Fleming方法显示出正确停止试验的最高概率(约95%)。在另一种情况下,贝叶斯方法显示正确停止ADRENAL试验的总体可能性最高(约91%),而Haybittle-Peto方法在NICE-SUGAR试验中获得了最大的影响力。随着间期数量的增加,治疗效果估计越来越被低估。
结论:这项研究证实了正确的适应性设计可以在样本量减少很多的情况下达到与固定设计相同的结论。与增加的interims数量相关的效率增益与具有大样本量和短随访时间的晚期重症监护试验高度相关。在试验设计阶段系统地探索自适应方法将有助于选择最合适的方法。
Adaptive clinical trials are growing in popularity as they are more flexible, efficient and ethical than traditional fixed designs. However, notwithstanding their increased use in assessing treatments for COVID-19, their use in critical care trials remains limited. A better understanding of the relative benefits of various adaptive designs may increase their use and interpretation.
Using two large critical care trials (ADRENAL.
gov number, NCT01448109. Updated 12-12-2017; NICE-SUGAR.
gov number, NCT00220987. Updated 01-29-2009), we assessed the performance of three frequentist and two bayesian adaptive approaches. We retrospectively re-analysed the trials with one, two, four, and nine equally spaced interims. Using the original hypotheses, we conducted 10,000 simulations to derive error rates, probabilities of making an early correct and incorrect decision, expected sample size and treatment effect estimates under the null scenario (no treatment effect) and alternative scenario (a positive treatment effect). We used a logistic regression model with 90-day mortality as the outcome and the treatment arm as the covariate. The null hypothesis was tested using a two-sided significance level (α) at 0.05.
Across all approaches, increasing the number of interims led to a decreased expected sample size. Under the null scenario, group sequential approaches provided good control of the type-I error rate; however, the type I error rate inflation was an issue for the Bayesian approaches. The Bayesian Predictive Probability and O\'Brien-Fleming approaches showed the highest probability of correctly stopping the trials (around 95%). Under the alternative scenario, the Bayesian approaches showed the highest overall probability of correctly stopping the ADRENAL trial for efficacy (around 91%), whereas the Haybittle-Peto approach achieved the greatest power for the NICE-SUGAR trial. Treatment effect estimates became increasingly underestimated as the number of interims increased.
This study confirms the right adaptive design can reach the same conclusion as a fixed design with a much-reduced sample size. The efficiency gain associated with an increased number of interims is highly relevant to late-phase critical care trials with large sample sizes and short follow-up times. Systematically exploring adaptive methods at the trial design stage will aid the choice of the most appropriate method.