We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCT) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g. unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted adaptive power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.

翻译：我们提出一种用于混合分析的方法，该方法利用外部对照来增强随机对照试验（RCT）的内部对照组，其借用程度基于RCT与外部对照患者之间的相似性确定，以考虑系统差异（如未测量的混杂因素）。该方法是对幂次先验的创新扩展，其中基于与随机对照数据的兼容性为每个外部对照单独计算折扣权重。该折扣权重通过RCT中估计的时间事件参数的后验分布推导出的外部对照预测分布确定。该方法采用具有分段常数基线风险的比例风险回归模型实施。基于一项已完成的非小细胞肺癌试验，展示了模拟研究和真实数据示例。结果表明，病例加权自适应幂次先验在外部对照与RCT人群存在各种形式的不兼容性时，仍能提供稳健的推断。