Wu Liwen, Wang Junyao, Wahed Abdus S
Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, MA.
Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY.
Stat Med. 2025 Feb 10;44(3-4):e10328. doi: 10.1002/sim.10328.
Clinical trials are often designed based on limited information about effect sizes and precision parameters with risks of underpowered studies. This is more problematic for SMARTs where strategy effects are based on sequences of treatments. Sample size adjustment offers flexibility through re-estimating sample size during the trial to ensure adequate power at the final analysis. While this adaptation is common for standard clinical trials, corresponding methods to perform sample size adjustment have not been adapted to SMARTs. In this paper, we propose a sample size adjustment procedure for SMARTs. Sample sizes are re-calculated at the interim analysis based on the conditional power derived from a bivariate non-central chi-square distribution. We demonstrate through simulation studies that even with an underpowered initial sample size due to miss-specified parameters at the design stage, the proposed method can maintain desirable power at the end of the study, and additional resources are only invested in trials that show promising conditional power at the interim analysis.
临床试验通常是基于关于效应大小和精度参数的有限信息设计的，存在研究效能不足的风险。对于序贯多重分配随机试验（SMARTs）而言，这一问题更为突出，因为策略效应是基于治疗序列的。样本量调整通过在试验期间重新估计样本量来提供灵活性，以确保在最终分析时有足够的效能。虽然这种调整在标准临床试验中很常见，但执行样本量调整的相应方法尚未适用于序贯多重分配随机试验。在本文中，我们提出了一种适用于序贯多重分配随机试验的样本量调整程序。在中期分析时，根据从双变量非中心卡方分布导出的条件效能重新计算样本量。我们通过模拟研究表明，即使由于设计阶段参数设定错误导致初始样本量效能不足，所提出的方法仍可在研究结束时保持理想的效能，并且仅将额外资源投入到在中期分析中显示出有前景的条件效能的试验中。
