There is growing interest in Bayesian clinical trial designs with informative prior distributions, e.g. for extrapolation of adult data to pediatrics, or use of external controls. While the classical type I error is commonly used to evaluate such designs, it cannot be strictly controlled and it is acknowledged that other metrics may be more appropriate. We focus on two common situations - borrowing control data or information on the treatment contrast - and discuss several fully probabilistic metrics to evaluate the risk of false positive conclusions. Each metric requires specification of a design prior, which can differ from the analysis prior and permits understanding of the behaviour of a Bayesian design under scenarios where the analysis prior differs from the true data generation process. The metrics include the average type I error and the pre-posterior probability of a false positive result. We show that, when borrowing control data, the average type I error is asymptotically (in certain cases strictly) controlled when the analysis and design prior coincide. We illustrate use of these Bayesian metrics with real applications, and discuss how they could facilitate discussions between sponsors, regulators and other stakeholders about the appropriateness of Bayesian borrowing designs for pivotal studies.

翻译：在贝叶斯临床试验设计中，使用信息性先验分布（例如：将成人数据外推至儿科，或使用外部对照）的兴趣日益增长。虽然经典第一类错误常被用于评估此类设计，但无法严格加以控制，且公认其他度量可能更为合适。我们聚焦于两种常见情形——借用对照数据或处理对比信息——并讨论了用于评估假阳性结论风险的几种全概率度量。每种度量需指定一个设计先验，该先验可区别于分析先验，从而允许理解在分析先验与真实数据生成过程不同的场景下贝叶斯设计的行为。这些度量包括平均第一类错误和假阳性结果的先验后验概率。我们证明，在借用对照数据时，当分析先验与设计先验一致时，平均第一类错误可渐近（某些情况下严格）受控。我们通过实际应用说明了这些贝叶斯度量的使用，并讨论了它们如何促进申办方、监管机构及其他利益相关者之间关于贝叶斯借用设计在关键性研究中适用性的讨论。