发表机构
Yale School of Public Health; Yale Cancer Center, Yale School of Medicine; Pfizer Inc.（耶鲁大学公共卫生学院; 耶鲁大学医学院耶鲁癌症中心; 辉瑞公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CARB是一种仅用汇总信息、基于基线协变量差异设定先验权重的框架，可在无外部患者协变量数据时实现透明稳健的外部数据借用，降低偏倚与I类错误并提高效率。
AI 中文摘要
借用外部对照数据可提高临床试验的效率，尤其在患者入组困难时。一个长期存在的挑战是如何系统且透明地预先确定借用程度。实际应用中，先验权重常通过启发式选择或模拟校准来获得期望的操作特性，这使得其科学合理性难以论证。此外，新试验设计时通常无法获取外部来源的患者层面协变量数据，因此无法采用患者层面调整方法。我们提出CARB（Covariate-Assessed Robust Borrowing，协变量评估稳健借用），这一框架将先验权重的设定形式化为设计阶段对基线兼容性的评估。CARB仅使用汇总信息，无需新试验的结局数据，即可量化新试验与各外部来源之间预先指定的基线协变量的差异。预先指定的映射将所得的不相似度量转换为稳健借用模型可交换分量的特定来源先验权重。模拟研究表明，在存在可观测和不可观测不兼容性的情况下，相较于固定借用，CARB可降低偏倚和I类错误膨胀，且当外部对照兼容时能提高效率。对晚期黑色素瘤试验的应用示例展示了从多个历史来源进行协变量知情借用，报告的基线特征中存在的明显差异作为警告信号，减少了借用量。当无法获取外部来源的患者层面协变量数据时，CARB提供了一种透明且可重复的谨慎借用方式。
英文摘要
Borrowing external control data can improve the efficiency of clinical trials, particularly when patient accrual is difficult. A persistent challenge is how to prespecify the degree of borrowing systematically and transparently. In practice, prior weights are often selected heuristically or calibrated through simulation to achieve desired operating characteristics, making them difficult to justify scientifically. Moreover, patient-level covariate data from external sources are rarely available when the new trial is designed, precluding patient-level adjustment methods. We propose CARB (Covariate-Assessed Robust Borrowing), a framework that formalizes prior-weight specification as a design-stage assessment of baseline compatibility. Using only aggregate information, CARB quantifies discrepancies in prespecified baseline covariates between the new trial and each external source, without using outcome data from the new trial. A prespecified mapping translates the resulting dissimilarity measure into a source-specific prior weight on the exchangeable component of a robust borrowing model. Simulation studies show that CARB reduces bias and type I error inflation relative to fixed borrowing under observed and unobserved incompatibility, while improving efficiency when external controls are compatible. An application to advanced melanoma trials illustrates covariate-informed borrowing from multiple historical sources. An apparent discrepancy in reported baseline characteristics serves as a warning signal that reduces borrowing. CARB provides a transparent and reproducible way to borrow cautiously when patient-level covariate data from external sources are unavailable.
