TAD-SIE: Sample Size Estimation for Clinical Randomized Controlled Trials using a Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator
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TAD-SIE：结合趋势自适应设计与合成干预估计的随机临床试验样本量确定
- 研究问题
- 在平行组随机临床试验中，初始平均治疗效应估计不准确会妨碍样本量规划；如何在试验约束下达到目标功效并控制显著性水平？
- 方法与思路
- 作者将利用合成干预模拟交叉试验的 SECRETS 检验，与专门设计的趋势自适应样本量调整结合，并设置无效性停止机制。
- 材料陈述的贡献
- 摘要称，TAD-SIE 在所述真实三期临床试验数据评估中达到常用目标功效与显著性水平，表现优于所比较的样本量算法。
- 阅读关注点
- 应核对合成干预的构造与假设、自适应增样及停止规则，以及重复检验的错误控制证明和真实试验数据评估的实施方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未说明完整校准条件与评估流程，单项试验数据的结果不能推广至所有试验。
原文摘要
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 appropriately 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 SECRETS (Subject-Efficient Clinical Randomized Controlled Trials using Synthetic Intervention) for hypothesis testing, which simulates a cross-over trial in order to boost power; doing so, 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 SECRETS given that SECRETS 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. 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 reaches typical target operating points of 80% or 90% power and 5% significance level in contrast to baseline algorithms that only get at best 59% power and 4% significance level.