Incorporating external data can improve the efficiency of clinical trials, but distributional mismatches between current and external populations threaten the validity of inference. While numerous dynamic borrowing methods exist, the calibration of their borrowing parameters relies mainly on ad hoc, simulation-based tuning. To overcome this, we propose BOND (Borrowing under Optimal Nonparametric Distributional robustness), a framework that formalizes data noncommensurability through Wasserstein ambiguity sets centered at the current-trial distribution. By deriving sharp, closed-form bounds on the worst-case mean drift for both continuous and binary outcomes, we construct a distributionally robust, bias-corrected Wald statistic that ensures asymptotic type I error control uniformly over the ambiguity set. Importantly, BOND determines the optimal borrowing strength by maximizing a worst-case power proxy, converting heuristic parameter tuning into a transparent, analytically tractable optimization problem. Furthermore, we demonstrate that many prominent borrowing methods can be reparameterized via an effective borrowing weight, rendering our calibration framework broadly applicable. Simulation studies and a real-world clinical trial application confirm that BOND preserves the nominal size under unmeasured heterogeneity while achieving efficiency gains over standard borrowing methods.

翻译：整合外部数据可提升临床试验效率，但当前研究人群与外部人群间的分布差异会威胁推断的有效性。尽管存在多种动态借用方法，其借用参数的校准主要依赖基于模拟的临时调参。为克服此局限，我们提出BOND（最优非参数分布鲁棒性下的借用框架），该框架通过以当前试验分布为中心的Wasserstein模糊集形式化表征数据不可公度性问题。通过推导连续型与二分类结局变量在最坏情况均值漂移上的尖锐闭式界，我们构建了具有分布鲁棒性的偏倚校正Wald统计量，确保在模糊集范围内一致控制渐近第一类错误。重要的是，BOND通过最大化最坏情况功效代理量来确定最优借用强度，将启发式参数调优转化为透明且可解析处理的优化问题。此外，我们证明众多主流借用方法可通过有效借用权重进行重参数化，使得本校准框架具有广泛适用性。模拟研究与真实临床试验应用证实，BOND在未测量异质性下能保持名义检验水准，同时较标准借用方法获得效率提升。