发表机构
Auburn University（奥本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出摊销式神经后验估计（NPE）作为贝叶斯动态借用的灵活替代方案，通过预训练网络实现快速后验推断，在结局漂移下显著降低偏差并加速计算，并在ADNI数据上验证了有效性。
AI 中文摘要
在药物开发中，由于入组缓慢、随访费用高昂，且往往已有密切相关的试验或真实世界数据，利用外部或历史队列扩充小型同期研究颇具吸引力。贝叶斯动态借用（BDB）为自适应控制外部数据的影响提供了原则性框架，但经典实现通常依赖于手工设定的先验和基于MCMC的推断，这可能计算成本高昂且缺乏泛化性。在本工作中，我们研究摊销式神经后验估计（NPE）作为一种灵活的替代方案。单个网络在模拟的当前/外部数据集对上进行预训练，这些数据集对涵盖协变量偏移、结局漂移和联合不可交换性，然后通过一次前向传播返回当前研究标量目标的近似后验。通过模拟研究，我们发现NPE在结局漂移和联合不匹配情形下最为有用：在更困难的结局漂移场景中，其绝对偏差比最佳经典基线低约五倍，且I类错误接近名义水平。预训练后，每个数据集获得后验摘要约需8毫秒，在我们的计时实验中，比基于MCMC的借用基线快约10^3倍。我们进一步分析了阿尔茨海默病神经影像学倡议（ADNI）数据，并表明当轻度认知障碍结局在不同队列间存在差异时，NPE公式在该示例中恢复了后期队列的风险水平，而未声称更高的精度。代码可在以下网址获取：此https URL。
英文摘要
Augmenting small concurrent studies with external or historical cohorts is attractive in drug development, where enrollment is slow, follow-up is expensive, and closely related trial or real-world data are often already available. Bayesian dynamic borrowing (BDB) provides a principled framework for adaptively controlling the influence of external data, but classical implementations often depend on hand-specified priors and MCMC-based inference, which can be computationally expensive and not generalizable. In this work, we study amortized neural posterior estimation (NPE) as a flexible alternative. A single network is pretrained on simulated current/external dataset pairs spanning covariate shift, outcome drift, and joint non-exchangeability, and then returns an approximate posterior for a scalar current-study target in a single forward pass. Through simulation studies, we find that NPE is most useful under outcome drift and joint mismatch: in the harder outcome-drift regimes, it gives up to about five-fold lower absolute bias than the best classical baseline and keeps Type I error close to nominal. After pretraining, posterior summaries are obtained in about 8 ms per dataset, roughly $10^3\times$ faster than MCMC-based borrowing baselines in our timing experiment. We further analyze Alzheimer's Disease Neuroimaging Initiative (ADNI) data and show that, when mild cognitive impairment outcomes differ across cohorts, the NPE formulation recovers the later-cohort risk level in this example without claiming greater precision. Code is available at https://github.com/ChinHungScott/NPE-for-Bayesian-Dynamic-Borrowing-MLHC-.
Comments27 pages. Accepted at the 11th Machine Learning for Healthcare Conference (MLHC 2026)
Journal refProceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:584-610, 2026
