Bayesian predictive probabilities are commonly used for interim monitoring of clinical trials through efficacy and futility stopping rules. Despite their usefulness, calculation of predictive probabilities, particularly in pre-experiment trial simulation, can be a significant challenge. We introduce an approximation for computing predictive probabilities using either a p-value or a posterior probability that significantly reduces this burden. We show the approximation has a high degree of concordance with standard Monte Carlo imputation methods for computing predictive probabilities, and present five simulation studies comparing the approximation to the full predictive probability for a range of primary analysis strategies: dichotomous, time-to-event, and ordinal endpoints, as well as historical borrowing and longitudinal modeling. We find that this faster method of predictive probability approximation works well in all five applications, thus significantly reducing the computational burden of trial simulation, allowing more virtual trials to be simulated to achieve greater precision in estimating trial operating characteristics.

翻译：贝叶斯预测概率通常通过有效性和无效性终止规则用于临床试验的中期监测。尽管其具有实用性，但预测概率的计算，尤其是在实验前试验模拟中，可能是一项重大挑战。我们引入了一种使用p值或后验概率计算预测概率的近似方法，该方法显著减轻了这一负担。我们证明该近似方法与用于计算预测概率的标准蒙特卡洛插补方法具有高度一致性，并提出了五项模拟研究，在一系列主要分析策略（包括二分类、时间-事件和有序终点，以及历史借入和纵向建模）中，将该近似方法与完整预测概率进行比较。我们发现这种更快速的预测概率近似方法在所有五项应用中均表现良好，从而显著降低了试验模拟的计算负担，允许模拟更多虚拟试验，以实现对试验操作特性估计的更高精度。