The integration of external data using Bayesian mixture priors has become a powerful approach in clinical trials, offering significant potential to improve trial efficiency. Despite their strengths in analytical tractability and practical flexibility, existing methods such as the robust meta-analytic-predictive (rMAP) and self-adapting mixture (SAM) often presume borrowing without rigorously assessing whether external information is appropriate to incorporate. When external and concurrent data are discordant, excessive borrowing can bias estimation and lead to misleading conclusions. To address this, we introduce WOW, a Kullback-Leibler-based gating strategy guided by the widely applicable information criterion (WAIC). Within the mixture-prior framework, WAIC-Optimized Weighting (WOW) conducts a preliminary compatibility assessment between external and concurrent trial data to determine eligibility for borrowing. Only if this gating criterion is satisfied does borrowing proceed; a downstream mixture prior procedure, using user-specified fixed or adaptive weights, can then be applied to determine the amount of borrowing. Simulation studies demonstrate that incorporating the WOW strategy before Bayesian mixture prior borrowing methods effectively mitigates excessive borrowing and improves estimation accuracy. A real-data illustration further highlights the feasibility and interpretability of the proposed gate-then-borrow strategy. By providing a practical safeguard against inappropriate borrowing, WOW strengthens the reliability of mixture-prior methods and supports better decision-making in clinical trials.

翻译：利用贝叶斯混合先验整合外部数据已成为临床试验中一种强大方法，具有显著提升试验效率的潜力。尽管现有方法如稳健元分析预测（rMAP）和自适应混合（SAM）在分析可处理性和实践灵活性方面具有优势，但它们通常假定借用而不严格评估外部信息是否适合纳入。当外部数据与同期数据不一致时，过度借用可能导致估计偏倚并得出误导性结论。为此，我们提出WOW——一种基于Kullback-Leibler散度且受广泛适用信息准则（WAIC）引导的门控策略。在混合先验框架内，WAIC优化加权（WOW）对外部与同期试验数据进行初步兼容性评估，以判定借用资格。仅当该门控准则满足时，借用才得以进行；随后可使用用户指定的固定或自适应权重，通过下游混合先验程序确定借用程度。模拟研究表明，在贝叶斯混合先验借用方法之前纳入WOW策略，可以有效缓解过度借用并提高估计精度。真实数据示例进一步凸显了所提出的"先门控后借用"策略的可行性与可解释性。通过为不当借用提供实用防护，WOW增强了混合先验方法的可靠性，并有助于临床试验中更优决策的制定。