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：整合外部数据能够提升临床试验的效率，但当前研究人群与外部人群之间的分布失配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于基于模拟的临时调整。为克服这一局限，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模糊集来形式化数据不可公度性问题。通过推导连续型和二分类结局变量在最坏情况均值漂移上的尖锐闭式界，我们构建了一个分布鲁棒的偏倚校正Wald统计量，确保在模糊集上一致地实现渐近第一类误差控制。重要的是，BOND通过最大化最坏情况功效代理指标来确定最优借用强度，从而将启发式参数调整转化为透明且可解析处理的优化问题。此外，我们证明许多主流借用方法可通过有效借用权重进行重新参数化，使得我们的校准框架具有广泛适用性。模拟研究和真实世界临床试验应用证实，BOND在存在未测量异质性的情况下能保持名义检验水准，同时相较于标准借用方法获得效率提升。