Bayesian clinical trials allow taking advantage of relevant external information through the elicitation of prior distributions, which influence Bayesian posterior parameter estimates and test decisions. However, incorporation of historical information can have harmful consequences on the trial’s frequentist (conditional) operating characteristics in case of inconsistency between prior information and the newly collected data. A compromise between meaningful incorporation of historical information and strict control of frequentist error rates is therefore often sought. Our aim is thus to review and investigate the rationale and consequences of different approaches to relaxing strict frequentist control of error rates from a Bayesian decision-theoretic viewpoint. In particular, we define an integrated risk which incorporates losses arising from testing, estimation, and sampling. A weighted combination of the integrated risk addends arising from testing and estimation allows moving smoothly between these two targets. Furthermore, we explore different possible elicitations of the test error costs, leading to test decisions based either on posterior probabilities, or solely on Bayes factors. Sensitivity analyses are performed following the convention which makes a distinction between the prior of the data-generating process, and the analysis prior adopted to fit the data. Simulation in the case of normal and binomial outcomes and an application to a one-arm proof-of-concept trial, exemplify how such analysis can be conducted to explore sensitivity of the integrated risk, the operating characteristics, and the optimal sample size, to prior-data conflict. Robust analysis prior specifications, which gradually discount potentially conflicting prior information, are also included for comparison. Guidance with respect to cost elicitation, particularly in the context of a Phase II proof-of-concept trial, is provided.

中文翻译：

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贝叶斯临床试验设计和评估对先前数据冲突的稳健性的决策理论方法。

贝叶斯临床试验允许通过引出影响贝叶斯后验参数估计和测试决策的先验分布来利用相关的外部信息。然而，如果先前信息与新收集的数据不一致，历史信息的结合可能会对试验的频繁（条件）操作特征产生有害影响。因此，经常寻求在有意义的历史信息合并和对频繁错误率的严格控制之间进行折衷。因此，我们的目标是从贝叶斯决策理论的角度审查和调查不同方法放松对错误率的严格频率控制的基本原理和后果。尤其是，我们定义了一个综合风险，它包含了测试、估计和抽样产生的损失。由测试和估计产生的综合风险加数的加权组合允许在这两个目标之间平稳移动。此外，我们探索了测试错误成本的不同可能诱因，从而导致基于后验概率或仅基于贝叶斯因子的测试决策。敏感性分析是按照惯例进行的，该惯例区分了数据生成过程的先验和为拟合数据而采用的分析先验。在正常和二项式结果的情况下进行模拟，并应用于单臂概念验证试验，举例说明如何进行此类分析以探索综合风险的敏感性、操作特征、和最优样本量，避免先验数据冲突。还包括稳健分析先验规范，这些规范逐渐降低潜在冲突的先验信息，以进行比较。提供了关于成本引出的指导，特别是在 II 期概念验证试验的背景下。