Randomized controlled clinical trials provide the gold standard for evidence generation in relation to the effectiveness of a new treatment in medical research. Relevant information from previous studies may be desirable to incorporate in the design of a new trial, with the Bayesian paradigm providing a coherent framework to formally incorporate prior knowledge. Many established methods involve the use of a discounting factor, sometimes related to a measure of `similarity' between historical sources and the new trial. However, it is often the case that the sample size is highly nonlinear in those discounting factors. This hinders communication with subject-matter experts to elicit sensible values for borrowing strength at the trial design stage. Focusing on a sample size formula that can incorporate historical data from multiple sources, we propose a linearization technique such that the sample size changes evenly over values of the discounting factors (hereafter referred to as `weights'). Our approach leads to interpretable weights that directly represent the dissimilarity between historical and new trial data on the probability scale, and could therefore facilitate easier elicitation of expert opinion on their values. Inclusion of historical data in the design of clinical trials is not common practice. Part of the reason might be difficulty in interpretability of discrepancy parameters. We hope our work will help to bridge this gap and encourage uptake of these innovative methods. Keywords: Bayesian sample size determination; Commensurate priors; Historical borrowing; Prior aggregation; Uniform shrinkage.

翻译：随机对照临床试验为医学研究中新疗法有效性的证据生成提供了金标准。理想情况下，可以将以往研究的相关信息纳入新试验的设计中，而贝叶斯范式为正式整合先验知识提供了连贯的框架。许多现有方法涉及使用折扣因子，该因子有时与历史来源和新试验之间的“相似性”度量相关。然而，样本量通常对这些折扣因子具有高度非线性关系，这阻碍了与领域专家沟通以在试验设计阶段合理确定借用强度的取值。针对一种可纳入多个来源历史数据的样本量公式，我们提出了一种线性化技术，使得样本量随折扣因子（以下简称“权重”）值的变化均匀改变。我们的方法产生了可解释的权重，这些权重直接在概率尺度上表示历史数据与新试验数据之间的不相似性，从而有助于更简便地征询专家对其取值的意见。在临床试验设计中纳入历史数据并非普遍做法，部分原因可能在于不一致参数的可解释性困难。我们希望我们的工作能弥合这一差距，并促进这些创新方法的采纳。关键词：贝叶斯样本量确定；可比先验；历史数据借用；先验聚合；均匀收缩