Bayesian predictive probabilities of success (PPoS) use interim trial data to calculate the probability of trial success. These quantities can be used to optimize trial size or to stop for futility. In this paper, we describe a simulation-based approach to compute the PPoS for clinical trials with competing event data, for which no specific methodology is currently available. The proposed procedure hinges on modelling the joint distribution of time to event and event type by specifying Bayesian models for the cause-specific hazards of all event types. This allows the prediction of outcome data at the conclusion of the trial. The PPoS is obtained by numerically averaging the probability of success evaluated at fixed parameter values over the posterior distribution of the parameters. Our work is motivated by two randomised clinical trials: the I-SPY COVID phase II trial for the treatment of severe COVID-19 (NCT04488081) and the STHLM3 prostate cancer diagnostic trial (ISRCTN84445406), both of which are characterised by competing event data. We present different modelling alternatives for the joint distribution of time to event and event type and show how the choice of the prior distributions can be used to assess the PPoS under different scenarios. The role of the PPoS analyses in the decision making process for these two trials is also discussed.

翻译：贝叶斯成功预测概率利用临床试验期中数据计算试验成功的概率。该指标可用于优化试验规模或提前终止无效试验。本文提出了一种基于模拟的方法，用于计算具有竞争事件数据的临床试验的成功预测概率，目前该领域尚无特定方法可用。所提方法的核心在于通过为所有事件类型的病因特异性风险建立贝叶斯模型，从而建模事件发生时间与事件类型的联合分布。这使得在试验结束时预测结局数据成为可能。成功预测概率通过将固定参数值下的成功概率在参数后验分布上进行数值平均而获得。我们的研究受到两项随机临床试验的启发：治疗重症COVID-19的I-SPY COVID II期试验（NCT04488081）和STHLM3前列腺癌诊断试验（ISRCTN84445406），这两项试验均具有竞争事件数据的特征。我们提出了事件发生时间与事件类型联合分布的不同建模方案，并展示了如何通过先验分布的选择来评估不同情境下的成功预测概率。本文还讨论了成功预测概率分析在这两项试验决策过程中的作用。