Qualify-Then-Borrow: A Five-Step Framework for Bayesian Borrowing Beyond Outcome Agreement

RESUMO

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.