Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-the-Losers Design

When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II–Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. To diminish the barriers associated with a Bayesian two-stage drop-the-losers design, we built a user-friendly point-and-click graphical user interface with <i>R Shiny</i> to aid researchers in planning such designs by allowing them to easily obtain trial operating characteristics, estimate statistical power and sample size, and optimize patient allocation in each stage to maximize power. We assume that endpoints are distributed normally with unknown but common variance between treatments. We recommend this software as an easy way to engage statisticians and researchers in two-stage designs as well as to actively investigate the power of two-stage designs relative to more traditional approaches. The software is freely available at https://github.com/stefangraw/Allocation-Power-Optimizer.

当研究者希望针对对照组检验多个试验组时，两阶段自适应设计可比单阶段设计更具效率——后者会将患者均等分配至所有试验组与对照组中。这类设计在临床试验中常被视作无缝Ⅱ期-Ⅲ期试验设计。相较于标准单阶段设计，这类设计需要更多统计学支撑，且在规划与分析层面更为复杂。为破解贝叶斯两阶段淘汰劣势组设计的应用壁垒，我们基于R Shiny开发了一款易用的点击式图形用户界面，助力研究者开展此类设计的规划工作：使用者可轻松获取试验操作特征、估算统计功效与样本量，并优化各阶段的患者分配方案以最大化统计功效。本研究假设试验终点服从正态分布，且各处理组间的方差未知但齐同。我们推荐使用这款软件，以便捷地推动统计学家与研究者参与两阶段设计的相关工作，并主动探究相较于传统方法的两阶段设计功效表现。该软件可于https://github.com/stefangraw/Allocation-Power-Optimizer免费获取。