We introduce Robust Bayesian Sequential Borrowing (RBSB), a framework for extrapolating evidence across adjacent subgroups in multi-population clinical programmes where studies are conducted in sequence and populations are ordered by clinical proximity. Conventional approaches weight all historical sources uniformly or exclude distant populations entirely, failing to reflect the natural gradient of similarity in such programmes. RBSB encodes the programme order through path-dependent borrowing via robust mixture priors that combine an informative component with a unit-information component to guard against prior-data conflict. Posterior weights, derived in closed form from marginal likelihood ratios, provide transparent dynamic attenuation when heterogeneity arises between sequential populations. The framework supports prospective evaluation of Bayesian Type I error, power, and extends naturally to assurance at both the study and programme level. Simulation studies demonstrate superior false-positive control relative to full pooling, while preserving substantial efficiency gains over standalone analyses. A case study of the START trial illustrates the approach across adult, adolescent, and paediatric populations. RBSB offers a practical, regulator-aligned method for disciplined evidence borrowing that exploits temporal and biological proximity while preventing implausible extrapolation across distant populations.

翻译：我们提出一种名为稳健贝叶斯序贯借用（RBSB）的框架，用于在多人群临床试验项目中对邻近亚组进行证据外推。此类项目以序贯方式进行研究，且人群按临床邻近性排序。传统方法对所有历史来源数据赋予统一权重，或完全排除远距离人群，未能反映此类项目中自然存在的相似性梯度。RBSB通过路径依赖的稳健混合先验分布对项目顺序进行编码，该先验将信息型分量与单位信息分量结合，以防范先验与数据间的冲突。基于边际似然比导出的后验权重以闭合形式呈现，当序贯人群间出现异质性时，可提供透明的动态衰减机制。该框架支持贝叶斯I类错误与检验效能的前瞻性评估，并自然延伸至研究层面及项目层面的保证概率。模拟研究表明，相较于完全合并方法，RBSB在保持相比于独立分析显著效率增益的同时，实现了更优的假阳性控制。通过START试验案例，我们展示了该方法在成人、青少年及儿童人群中的应用。RBSB提供了一种实用、符合监管要求的证据借用方法，既利用了时间与生物邻近性，又防止了远距离人群中不合理的过度外推。