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将功效函数中未知的总体水平参数替换为外部研究中观察到的数值来实现的。许多作者和实践者将此视为功效的假定值，并提出以贝叶斯量——成功概率或保证度——作为替代方案。其主张是，通过对先验或后验分布进行平均，成功概率能够超越传统功效计算，因为它捕捉了未知真实治疗效果及其他总体水平参数的不确定性。我们利用p值函数框架，论证了成功概率计算与典型功效计算本质上仅产生了两种不同的功效点估计。我们证明，基于任一种功效点估计的“继续/终止”决策均不能充分量化与控制相关风险；相反，我们主张应采用基于功效推断的“继续/终止”决策，以实现更优的风险管理与决策制定。