Lala Sayeri, Jha Niraj K
Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA.
Contemp Clin Trials Commun. 2024 Feb 3;38:101265. doi: 10.1016/j.conctc.2024.101265. eCollection 2024 Apr.
The parallel-group randomized controlled trial (RCT) is commonly used in Phase-3 clinical trials to establish treatment effectiveness but requires hundreds-to-thousands of subjects, making it difficult to implement, which leads to high Phase-3 trial failure rates. One approach to increasing power of a trial is to augment data collected from an RCT with external data from prospective studies or prior RCTs. However, this requires that external data be comparable to data from the study of interest, a condition that does not hold for new interventions or populations being studied. Another approach is to lower sample size requirements by using the cross-over design, which measures individual treatment effects (ITEs) to remove inter-subject variability; however, this design is only suitable for chronic conditions and interventions with effects that wash out rapidly.
We propose a novel and practical framework called SECRETS (Subject-Efficient Clinical Randomized Controlled Trials using Synthetic Intervention) to increase power of any parallel-group RCT by simulating the cross-over design using only data collected from the study. SECRETS first estimates ITEs across all subjects recruited to the RCT by using a state-of-the-art counterfactual estimation algorithm called synthetic intervention (SI). Since SI induces dependencies among the ITEs, we introduce a novel hypothesis testing strategy to test for treatment effectiveness.
We show that SECRETS can increase the power of an RCT while maintaining comparable significance levels; in particular, on three real-world clinical RCTs (Phase-3 trials), SECRETS increases power over the baseline method by % (average: 21.5%, standard deviation: 15.8%), thereby reducing the number of subjects needed to obtain a typically desired statistical operating point of 80% power and 5% significance level by % (10-3,957 fewer subjects per arm). Our analyses show that SECRETS increases power by consistently reducing the variance of the average treatment effect, thereby mimicking the effects of a cross-over design.
SECRETS increases subject efficiency of an RCT by simulating the cross-over design using only data collected from the RCT; therefore, it is a feasible solution for increasing the trial's power, especially under settings where satisfying sample size requirements is difficult.
平行组随机对照试验（RCT）常用于三期临床试验以确定治疗效果，但需要数百至数千名受试者，实施起来较为困难，这导致三期试验失败率很高。提高试验效能的一种方法是用前瞻性研究或既往RCT的外部数据补充从RCT收集的数据。然而，这要求外部数据与感兴趣研究的数据具有可比性，对于新的干预措施或正在研究的人群，这一条件并不成立。另一种方法是采用交叉设计来降低样本量要求，该设计测量个体治疗效果（ITE）以消除受试者间的变异性；然而，这种设计仅适用于慢性疾病和效果迅速消失的干预措施。
我们提出了一种新颖实用的框架，称为SECRETS（使用合成干预的高效受试者临床随机对照试验），通过仅使用从研究中收集的数据模拟交叉设计来提高任何平行组RCT的效能。SECRETS首先使用一种称为合成干预（SI）的先进反事实估计算法估计招募到RCT的所有受试者的ITE。由于SI会在ITE之间引发相关性，我们引入了一种新颖的假设检验策略来检验治疗效果。
我们表明，SECRETS可以提高RCT的效能，同时保持可比的显著性水平；特别是在三项真实世界的临床RCT（三期试验）中，SECRETS比基线方法将效能提高了%（平均：21.5%，标准差：15.8%），从而将获得通常期望的80%效能和5%显著性水平的统计操作点所需的受试者数量减少了%（每臂减少10 - 3957名受试者）。我们的分析表明，SECRETS通过持续降低平均治疗效果的方差来提高效能，从而模拟交叉设计的效果。
SECRETS通过仅使用从RCT收集的数据模拟交叉设计来提高RCT的受试者效率；因此，它是提高试验效能的可行解决方案，尤其是在难以满足样本量要求的情况下。
