样本量测定
计算机科学
时间轴
样品(材料)
代理终结点
特征选择
估计
选择(遗传算法)
I类和II类错误
可靠性工程
统计
机器学习
医学
数学
工程类
系统工程
内科学
化学
色谱法
作者
Zizhong Tian,Liwen Wu,Rachael Liu,Jianchang Lin
摘要
To support the expedited drug development addressing unmet medical needs, the seamless phase 2/3 design that makes the phase switching decision based on early surrogate endpoints is gaining popularity in practice. For also catering to potentially more beneficial patient subgroups based on predictive biomarkers, it is appealing to incorporate the subgroup enrichment feature into the seamless phase 2/3 design. However, the sample size planning for such a complex adaptive design is challenging, as it must strike a balance among shortening development timeline, mitigating development risks, and accounting for uncertainty related to subgroup effects. To fill this gap, we propose a flexible seamless phase 2/3 design framework with population selection and sample size re-estimation using surrogate endpoints. We elucidate the patterns of the overall type I error for the proposed adaptive design and propose an easy-to-implement approach to control the overall type I error. Extensive simulation studies are conducted to demonstrate the advantages of our proposal design compared to the fixed-sample design in terms of efficiency, power, and timeline saving. We also illustrate our proposed design in a case study for relapsed/refractory multiple myeloma.
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