BACKGROUND When conducing Phase-III trial, regulatory agencies and investigators might want to get reliable information about rare but serious safety outcomes during the trial. Bayesian non-inferiority approaches have been developed, but commonly utilize historical placebo-controlled data to define the margin, depend on a single final analysis, and no recommendation is provided to define the prespecified decision threshold. In this study, we propose a non-inferiority Bayesian approach for sequential monitoring of rare dichotomous safety events incorporating experts' opinions on margins. METHODS A Bayesian decision criterion was constructed to monitor four safety events during a non-inferiority trial conducted on pregnant women at risk for premature delivery. Based on experts' elicitation, margins were built using mixtures of beta distributions that preserve experts' variability. Non-informative and informative prior distributions and several decision thresholds were evaluated through an extensive sensitivity analysis. The parameters were selected in order to maintain two rates of misclassifications under prespecified rates, that is, trials that wrongly concluded an unacceptable excess in the experimental arm, or otherwise. RESULTS The opinions of 44 experts were elicited about each event non-inferiority margins and its relative severity. In the illustrative trial, the maximal misclassification rates were adapted to events' severity. Using those maximal rates, several priors gave good results and one of them was retained for all events. Each event was associated with a specific decision threshold choice, allowing for the consideration of some differences in their prevalence, margins and severity. Our decision rule has been applied to a simulated dataset. CONCLUSIONS In settings where evidence is lacking and where some rare but serious safety events have to be monitored during non-inferiority trials, we propose a methodology that avoids an arbitrary margin choice and helps in the decision making at each interim analysis. This decision rule is parametrized to consider the rarity and the relative severity of the events and requires a strong collaboration between physicians and the trial statisticians for the benefit of all. This Bayesian approach could be applied as a complement to the frequentist analysis, so both Data Safety Monitoring Boards and investigators can benefit from such an approach.

中文翻译：

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使用专家边际启发的贝叶斯非劣性方法 - 应用于安全事件监控。

背景 在进行 III 期试验时，监管机构和研究人员可能希望获得有关试᪌期间罕见但严重的安全结果的可靠信息。贝叶�-96�非劣效方法已经开发出来，但通常利用历史安慰剂对照数据来定义裕度，依赖于单个最终分析，并且没有提供任何建议来定义预先指定的决策阈值。在本研究中，我们提出了一种非劣效贝叶斯方法，用于结合专家对边际的意见来连续监测罕见的二分安全事件。方法 构建贝叶斯决策标准来监测对有早产风险的孕妇进行的非劣效性试验中的四个安全事件。根据专家的启发，使用保留专家可变性的贝塔分布的混合来建立边际。通过广泛的敏感性分析评估了非信息性和信息性先验分布以及几个决策阈值。选择参数是为了在预先指定的比率下维持两种错误分类率，即，错误地得出实验组中不可接受的过量结论的试验，或其他。结果 征求了 44 位专家对每个事件的非劣效性界限及其相对严重性的意见。在说明性试验中，最大错误分类率根据事件的严重程度进行调整。使用这些最大速率，几个先验给出了良好的结果，其中一个被保留用于所有事件。每个事件都与特定的决策阈值选择相关，允许考虑其发生率、边缘和严重性的一些差异。我们的决策规则已应用于模拟数据集。结论 在缺乏证据以及在非劣效性试验期间必须监测一些罕见但严重的安全事件的情况下，我们提出了一种方法，可以避免任意的裕度选择，并有助于每次中期分析的决策。该决策规则经过参数化，以考虑事件的罕见性和相对严重性，并需要医生和试验统计学家之间的密切合作，以造福所有人。这种贝叶斯方法可以作为频率分析的补充，因此数据安全监测委员会和调查人员都可以从这种方法中受益。