To investigate intervention effects on rare events, meta-analysis techniques are commonly applied in order to assess the accumulated evidence. When it comes to adverse effects in clinical trials, these are often most adequately handled using survival methods. A common-effect model that is able to process data in commonly quoted formats in terms of hazard ratios has been proposed for this purpose. In order to accommodate potential heterogeneity between studies, we have extended the model by Holzhauer to a random-effects approach. The Bayesian model is described in detail, and applications to realistic data sets are discussed along with sensitivity analyses and Monte Carlo simulations to support the conclusions.

翻译：为研究干预措施对罕见事件的影响，元分析技术常被用于评估累积证据。在处理临床试验中的不良事件时，生存分析方法通常最为适用。为此，已有研究提出一种能够处理以风险比形式呈现的常用数据格式的固定效应模型。为适应研究间潜在的异质性，我们将Holzhauer提出的模型扩展为随机效应方法。本文详细描述了该贝叶斯模型，并结合敏感性分析和蒙特卡洛模拟，讨论了其在真实数据集上的应用，以支撑研究结论。