BACKGROUND/AIMS Regulatory approval of a drug or device involves an assessment of not only the benefits but also the risks of adverse events associated with the therapeutic agent. Although randomized controlled trials (RCTs) are the gold standard for evaluating effectiveness, the number of treated patients in a single RCT may not be enough to detect a rare but serious side effect of the treatment. Meta-analysis plays an important role in the evaluation of the safety of medical products and has advantage over analyzing a single RCT when estimating the rate of adverse events. METHODS In this article, we compare 15 widely used meta-analysis models under both Bayesian and frequentist frameworks when outcomes are extremely infrequent or rare. We present extensive simulation study results and then apply these methods to a real meta-analysis that considers RCTs investigating the effect of rosiglitazone on the risks of myocardial infarction and of death from cardiovascular causes. RESULTS Our simulation studies suggest that the beta hyperprior method modeling treatment group-specific parameters and accounting for heterogeneity performs the best. Most models ignoring between-study heterogeneity give poor coverage probability when such heterogeneity exists. In the data analysis, different methods provide a wide range of log odds ratio estimates between rosiglitazone and control treatments with a mixed conclusion on their statistical significance based on 95% confidence (or credible) intervals. CONCLUSION In the rare event setting, treatment effect estimates obtained from traditional meta-analytic methods may be biased and provide poor coverage probability. This trend worsens when the data have large between-study heterogeneity. In general, we recommend methods that first estimate the summaries of treatment-specific risks across studies and then relative treatment effects based on the summaries when appropriate. Furthermore, we recommend fitting various methods, comparing the results and model performance, and investigating any significant discrepancies among them.

中文翻译：

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随机临床试验中罕见不良事件的荟萃分析：贝叶斯和频率论方法

背景/目的 药物或设备的监管批准不仅涉及对治疗剂相关不良事件的益处的评估，还涉及对不良事件风险的评估。尽管随机对照试验 (RCT) 是评估有效性的金标准，但单个 RCT 中接受治疗的患者数量可能不足以检测治疗的罕见但严重的副作用。Meta 分析在评估医疗产品的安全性方面发挥着重要作用，并且在估计不良事件发生率时比分析单个 RCT 具有优势。方法在本文中，我们在贝叶斯和频率论框架下比较了 15 个广泛使用的元分析模型，当结果极少或罕见时。我们展示了广泛的模拟研究结果，然后将这些方法应用于真正的荟萃分析，该荟萃分析考虑了研究罗格列酮对心肌梗塞和心血管原因死亡风险影响的随机对照试验。结果 我们的模拟研究表明，对治疗组特定参数进行建模并考虑异质性的 beta hyperprior 方法表现最佳。当存在这种异质性时，大多数忽略研究间异质性的模型会给出较差的覆盖概率。在数据分析中，不同的方法提供了罗格列酮和对照治疗之间广泛的对数优势比估计值，并基于 95% 置信（或可信）区间对其统计显着性得出了混合结论。结论 在罕见的事件设置中，从传统的元分析方法获得的治疗效果估计可能有偏差，并且覆盖概率很低。当数据具有较大的研究间异质性时，这种趋势会恶化。一般而言，我们推荐的方法首先估计跨研究的特定治疗风险摘要，然后在适当时根据摘要估计相对治疗效果。此外，我们建议拟合各种方法，比较结果和模型性能，并调查它们之间的任何显着差异。我们推荐的方法首先估计跨研究的特定治疗风险的总结，然后在适当的时候根据总结估计相对治疗效果。此外，我们建议拟合各种方法，比较结果和模型性能，并调查它们之间的任何显着差异。我们推荐的方法首先估计跨研究的特定治疗风险的总结，然后在适当的时候根据总结估计相对治疗效果。此外，我们建议拟合各种方法，比较结果和模型性能，并调查它们之间的任何显着差异。