Physical experiments in the national security domain are often expensive and time-consuming. Test engineers must certify the compatibility of aircraft and their weapon systems before they can be deployed in the field, but the testing required is time consuming, expensive, and resource limited. Adopting Bayesian adaptive designs are a promising way to borrow from the successes seen in the clinical trials domain. The use of predictive probability (PP) to stop testing early and make faster decisions is particularly appealing given the aforementioned constraints. Given the high-consequence nature of the tests performed in the national security space, a strong understanding of new methods is required before being deployed. Although PP has been thoroughly studied for binary data, there is less work with continuous data, which often in reliability studies interested in certifying the specification limits of components. A simulation study evaluating the robustness of this approach indicate early stopping based on PP is reasonably robust to minor assumption violations, especially when only a few interim analyses are conducted. A post-hoc analysis exploring whether release requirements of a weapon system from an aircraft are within specification with desired reliability resulted in stopping the experiment early and saving 33% of the experimental runs.

翻译：国家安全领域中的物理实验通常成本高昂且耗时。试飞工程师必须在飞机及其武器系统部署前认证其兼容性，但所需测试耗时、昂贵且资源有限。采用贝叶斯自适应设计是借鉴临床试验领域成功经验的一种有前景的方法。鉴于上述限制，利用预测概率（PP）提前停止测试并快速决策尤为引人关注。考虑到国家安全领域测试的高风险特性，新的方法在部署前需被充分理解。尽管预测概率在二元数据中已得到充分研究，但在连续数据中的应用尚不充分——而连续数据常用于组件规范限值认证的可靠性研究中。一项评估该方法鲁棒性的模拟研究表明，基于PP的提前停止策略对轻微假设违背具有合理鲁棒性，尤其在仅进行少量中期分析时。一项探索武器系统从飞机分离需求是否满足规范要求及其期望可靠性的事后分析显示，实验被提前终止并节省了33%的实验批次。