通过推断功率进行药物开发中的决策
Decision Making in Drug Development via Inference on Power
AI总结：
本文通过推断功率方法改进药物开发中的决策，指出传统功率计算和成功概率的平均方法无法有效控制风险，提出基于推断功率的决策方法以提升风险管理和决策质量。
摘要：
典型的功率计算是通过将功率函数中的未知群体层面量替换为外部研究中观察到的值进行的。许多作者和从业者将此视为假设的功率值，并提供贝叶斯量成功概率或保证作为替代方案。主张通过平均先验或后验分布，成功概率超越功率，通过捕捉未知真实治疗效应和任何其他群体层面参数的不确定性。我们使用p值函数将成功概率计算和典型功率计算视为仅产生两个不同的功率点估计。我们证明基于任一点估计值的Go/No-Go决策无法充分量化和控制风险，因此主张使用基于推断功率的Go/No-Go决策以实现更好的风险管理和决策制定。
英文摘要：
A typical power calculation is performed by replacing unknown population-level quantities in the power function with what is observed in external studies. Many authors and practitioners view this as an assumed value of power and offer the Bayesian quantity probability of success or assurance as an alternative. The claim is by averaging over a prior or posterior distribution, probability of success transcends power by capturing the uncertainty around the unknown true treatment effect and any other population-level parameters. We use p-value functions to frame both the probability of success calculation and the typical power calculation as merely producing two different point estimates of power. We demonstrate that Go/No-Go decisions based on either point estimate of power do not adequately quantify and control the risk involved, and instead we argue for Go/No-Go decisions that utilize inference on power for better risk management and decision making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
