发表机构
University Medical Center Göttingen; Deartment of Medical Statistics（哥廷根大学医学中心; 医学统计学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文讨论Calderazzo等人关于贝叶斯借用外部对照数据的试验设计中I型错误率膨胀的论文，指出其条件化方案实际意义有限，并建议通过调整似然函数来应对假设违背问题。
AI 中文摘要
基于某些固定条件的频率学派I型错误或功效的考量可能无法完全体现贝叶斯分析的目标；在涉及信息性先验的情况下尤其如此。Calderazzo等人考虑了涉及从外部对照数据贝叶斯借用的检验程序的适应性调整。在此处可能需要特别谨慎，以避免同时以参数和数据为条件。我们考察了他们的示例案例，并论证：(a) 该条件化方案可能具有有限的实际意义，且(b) 如果主要关注的是假设违背，那么这些问题或许更适合通过调整似然函数来解决。
英文摘要
Consideration of frequentist type-I error or power based on certain fixed conditions may not fully embrace the aims of a Bayesian analysis; this is particularly true in settings where informative priors are involved. Calderazzo et al. consider adaptations of a testing procedure involving Bayesian borrowing from external control data. Particular caution may be necessary here in order not to condition on parameters and data simultaneously. We consider their example case and argue that (a) the conditioning scheme may have limited practical implications, and (b) if the main concern are assumption violations, these might be better addressed by adapting the likelihood function.
Comments4 pages, 2 figures
