It is becoming increasingly popular to elicit informative priors on the basis of historical data. Popular existing priors, including the power prior, commensurate prior, and robust meta-analytic prior provide blanket discounting. Thus, if only a subset of participants in the historical data are exchangeable with the current data, these priors may not be appropriate. In order to combat this issue, propensity score (PS) approaches have been proposed. However, PS approaches are only concerned with the covariate distribution, whereas exchangeability is typically assessed with parameters pertaining to the outcome. In this paper, we introduce the latent exchangeability prior (LEAP), where observations in the historical data are classified into exchangeable and non-exchangeable groups. The LEAP discounts the historical data by identifying the most relevant subjects from the historical data. We compare our proposed approach against alternative approaches in simulations and present a case study using our proposed prior to augment a control arm in a phase 3 clinical trial in plaque psoriasis with an unbalanced randomization scheme.

翻译：摘要：基于历史数据构建信息先验的做法日益流行。现有的主流先验方法（如幂先验、相称先验及稳健元分析先验）均采用整体折扣策略，因此当历史数据中仅部分参与者的信息与当前数据具有可交换性时，这些先验可能不适用。为解决该问题，倾向性评分（PS）方法应运而生。然而，PS方法仅关注协变量分布，而可交换性通常需通过结果相关参数进行评估。本文提出潜在可交换性先验（LEAP），将历史数据中的观测值划分为可交换组与非可交换组。通过识别历史数据中最相关的个体，LEAP实现了对历史数据的差异化折扣。我们通过仿真实验将所提方法与其他替代方法进行比较，并基于斑块状银屑病III期临床试验案例（采用非平衡随机化方案），展示了LEAP先验在增强对照臂分析中的应用。