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.

翻译：典型的功效计算是通过用外部研究中的观测值替代功效函数中未知的总体水平参数来完成的。许多作者和实践者将其视为功效的假定值，并提出贝叶斯量——成功概率或保证作为替代方案。其主张是，通过对先验分布或后验分布进行平均，成功概率通过捕捉未知真实治疗效果及其他总体水平参数的不确定性而超越了功效。我们使用p值函数将成功概率计算和典型功效计算均框架化为仅产生两种不同的功效点估计。我们证明，基于任何一种功效点估计的Go/No-Go决策都无法充分量化并控制所涉及的风险，相反，我们主张利用功效推断做出Go/No-Go决策，以实现更好的风险管理与决策制定。