TAD-SIE: sample size estimation for clinical randomized controlled trials using a Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator.

IF 2 4区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
Trials Pub Date : 2025-01-29 DOI:10.1186/s13063-024-08661-1
Sayeri Lala, Niraj K Jha
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引用次数: 0

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

TAD-SIE:使用趋势自适应设计和基于综合干预的估计器进行临床随机对照试验的样本量估计。
背景:iii期临床试验通过实施大型随机对照试验(rct),为市场批准所需的药物安全性和有效性提供最高水平的证据。然而,这些试验中有30-40%失败,主要是因为这些研究的样本量不足,无法获得对平均治疗效果参数的准确初步估计。方法:为了消除药物开发周期中的这一障碍,我们提出了一种名为趋势自适应设计的新算法,该算法采用基于综合干预的估计器(TAD- sie),通过利用最先进的假设检验策略和新颖的趋势自适应设计(TAD),为平行组试验(标准RCT设计)提供动力。具体来说,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,这使得试验更容易在试验约束(例如样本量限制)下达到目标功率。为了估计样本大小,TAD- sie实现了一个针对SI的新TAD,因为使用它违反了标准TAD下的假设。此外,我们的TAD克服了标准TAD的无效性,允许在没有任何条件的情况下跨迭代增加样本量,同时控制无意义停止的显著性水平。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。结果:在现实世界的3期临床随机对照试验(即每组受试者数量相等的两组平行组优势试验)中,与基线算法相比,TAD-SIE获得的操作点数在63%至84%的功率和3%至6%的显著性水平之间,而基线算法最多获得49%的功率和6%的显著性水平。结论:TAD- sie是一种优良的TAD,可用于达到典型的目标操作点,但由于其序列性,仅适用于具有快速测量主要结果的试验。该框架对有意利用SI算法进行研究设计的实践者很有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Trials
Trials 医学-医学:研究与实验
CiteScore
3.80
自引率
4.00%
发文量
966
审稿时长
6 months
期刊介绍: Trials is an open access, peer-reviewed, online journal that will encompass all aspects of the performance and findings of randomized controlled trials. Trials will experiment with, and then refine, innovative approaches to improving communication about trials. We are keen to move beyond publishing traditional trial results articles (although these will be included). We believe this represents an exciting opportunity to advance the science and reporting of trials. Prior to 2006, Trials was published as Current Controlled Trials in Cardiovascular Medicine (CCTCVM). All published CCTCVM articles are available via the Trials website and citations to CCTCVM article URLs will continue to be supported.
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