Lala Sayeri, Jha Niraj K
Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA.
Trials. 2025 Jan 29;26(1):31. doi: 10.1186/s13063-024-08661-1.
Phase-3 clinical trials provide the highest level of evidence on drug safety and effectiveness needed for market approval by implementing large randomized controlled trials (RCTs). However, 30-40% of these trials fail mainly because such studies have inadequate sample sizes, stemming from the inability to obtain accurate initial estimates of average treatment effect parameters.
To remove this obstacle from the drug development cycle, we present a new algorithm called Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator (TAD-SIE) that powers a parallel-group trial, a standard RCT design, by leveraging a state-of-the-art hypothesis testing strategy and a novel trend-adaptive design (TAD). Specifically, TAD-SIE uses synthetic intervention (SI) to estimate individual treatment effects and thereby simulate a cross-over design, which makes it easier for a trial to reach target power within trial constraints (e.g., sample size limits). To estimate sample sizes, TAD-SIE implements a new TAD tailored to SI given that using it violates assumptions under standard TADs. In addition, our TAD overcomes the ineffectiveness of standard TADs by allowing sample sizes to be increased across iterations without any condition while controlling significance level with futility stopping. Our TAD also introduces a hyperparameter that enables trial designers to trade off between accuracy and efficiency (sample size and number of iterations) of the solution.
On a real-world Phase-3 clinical RCT (i.e., a two-arm parallel-group superiority trial with an equal number of subjects per arm), TAD-SIE obtains operating points ranging between 63% to 84% power and 3% to 6% significance level in contrast to baseline algorithms that get at best 49% power and 6% significance level.
TAD-SIE is a superior TAD that can be used to reach typical target operating points but only for trials with rapidly measurable primary outcomes due to its sequential nature. The framework is useful to practitioners interested in leveraging the SI algorithm for their study design.
三期临床试验通过开展大型随机对照试验（RCT），为药物上市批准所需的药物安全性和有效性提供了最高级别的证据。然而，这些试验中有30%-40%失败，主要原因是此类研究样本量不足，这源于无法获得平均治疗效果参数的准确初始估计值。
为了在药物研发周期中消除这一障碍，我们提出了一种名为基于合成干预估计器的趋势自适应设计（TAD-SIE）的新算法，该算法通过利用一种先进的假设检验策略和一种新颖的趋势自适应设计（TAD），为平行组试验（一种标准的RCT设计）提供动力。具体而言，TAD-SIE使用合成干预（SI）来估计个体治疗效果，从而模拟交叉设计，这使得试验在试验限制条件（如样本量限制）内更容易达到目标效能。为了估计样本量，鉴于使用SI会违反标准TAD的假设，TAD-SIE实施了一种针对SI量身定制的新TAD。此外，我们的TAD克服了标准TAD的无效性，通过允许在各次迭代中增加样本量而无需任何条件，同时通过无效性停止控制显著性水平。我们的TAD还引入了一个超参数，使试验设计者能够在解决方案的准确性和效率（样本量和迭代次数）之间进行权衡。
在一项真实世界的三期临床RCT（即双臂平行组优效性试验，每组受试者数量相等）中，与基线算法相比，TAD-SIE获得的操作点效能在63%至84%之间，显著性水平在3%至6%之间，而基线算法的最佳效能为49%，显著性水平为6%。
TAD-SIE是一种优越的TAD，由于其顺序性，可用于达到典型的目标操作点，但仅适用于主要结局可快速测量的试验。该框架对有兴趣在其研究设计中利用SI算法的从业者很有用。
