先合格后借用：超越结果一致性的贝叶斯借用五步框架
Qualify-Then-Borrow: A Five-Step Framework for Bayesian Borrowing Beyond Outcome Agreement
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中文总结 AI 辅助
QTB提出五步决策框架，通过科学来源评估决定外部数据是否借用、修复或排除，再以动态借用处理残差兼容性，避免仅依赖结果一致性导致的错误借用。
中文摘要 AI 辅助
目的：动态贝叶斯借用方法根据外部信息与当前数据的一致性或不一致性来调整其贡献。先合格后借用（QTB）明确说明了科学来源评估如何决定哪些外部信息进入借用模型，以及何时观察到的结果一致性应决定借用强度。方法：QTB 使用五个步骤：定义目标；评估外部数据；将其分类为合格、可修复或不合格；直接使用、修复后使用或排除它们；然后应用选定的贝叶斯借用方法。动态借用是最后一步，在已解决已识别的实质性差异后，基于残差经验兼容性进行操作。我们评估了82个预设场景，每个场景进行10,000次重复。结果：仅凭结果数据无法确定适当的借用决策。两个具有相同外部结果分布的场景产生了相似的稳健混合行为，但QTB决策不同，因为其中一个存在已知的终点问题。当借用错误分类的数据时，完全合并将95%覆盖率降至60.8%；稳健混合降权将覆盖率提高至91.8%，但留下了-4.8个百分点的偏差。对测量的总体偏移进行修复，将绝对偏差从1.94个百分点降至0.80个百分点。在强负残差漂移下，尽管进行了降权，I型错误仍达到10.0%。在完全兼容的数据下，QTB简化为通常的稳健混合分析。结论：QTB是一个五步决策架构，而非新的先验或兼容性统计量。它确定外部信息是原样进行、需要修复还是被排除，然后动态借用基于残差经验兼容性进行操作。合格并不证明可交换性，降权是对残差偏差的保障而非保证。
英文摘要
PURPOSE: Dynamic Bayesian borrowing methods adapt the contribution of external information according to agreement or disagreement with current data. Qualify-Then-Borrow (QTB) makes explicit how scientific source assessment determines what external information reaches the borrowing model and when observed outcome agreement should determine borrowing strength. METHODS: QTB uses five steps: define the target; assess the external data; classify them as qualified, repairable, or not qualified; use them directly, repair them before use, or exclude them; and then apply the selected Bayesian borrowing method. Dynamic borrowing is the final step and operates on residual empirical compatibility after identified material differences have been addressed. We evaluated 82 prespecified scenarios with 10,000 replications each. RESULTS: Outcome data alone could not determine the appropriate borrowing decision. Two settings with the same external outcome distribution produced similar robust-mixture behavior but different QTB decisions because one had a known endpoint problem. When misclassified data were borrowed, full pooling reduced 95% coverage to 60.8%; robust-mixture downweighting improved coverage to 91.8% but left a -4.8 percentage-point bias. Repair of a measured population shift reduced absolute bias from 1.94 to 0.80 percentage points. Under strong negative residual drift, Type I error reached 10.0% despite downweighting. With fully compatible data, QTB reduced to the usual robust-mixture analysis. CONCLUSION: QTB is a five-step decision architecture, not a new prior or compatibility statistic. It determines whether external information proceeds unchanged, requires repair, or is excluded before dynamic borrowing operates on residual empirical compatibility. Qualification does not certify exchangeability, and downweighting is a safeguard rather than a guarantee against residual bias.
发表机构
- St. Jude Children's Research Hospital（圣裘德儿童研究医院）
机构由 AI 辅助整理，请以论文原文为准。
