Department of Biostatistics & Bioinformatics, School of Medicine, Duke University, Durham, NC, USA.
The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.
Clin Trials. 2021 Feb;18(1):3-16. doi: 10.1177/1740774520969136. Epub 2020 Dec 1.
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.
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.
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.
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.
背景/目的：药物或器械的监管批准不仅涉及对与治疗剂相关的不良事件的益处进行评估，还涉及对风险进行评估。虽然随机对照试验（RCT）是评估有效性的金标准，但单个 RCT 中治疗的患者数量可能不足以发现治疗的罕见但严重的副作用。荟萃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的发生率方面优于分析单个 RCT。
在本文中，我们在贝叶斯和频率主义框架下比较了 15 种广泛使用的荟萃分析模型，当结局非常罕见或罕见时。我们呈现了广泛的模拟研究结果，然后将这些方法应用于一个真实的荟萃分析，该分析考虑了调查罗格列酮对心肌梗死风险和心血管原因死亡风险的 RCT。
我们的模拟研究表明，针对治疗组特定参数建模并考虑异质性的β超先验方法表现最佳。当存在这种异质性时，大多数忽略研究间异质性的模型给出的覆盖率概率较差。在数据分析中，不同的方法提供了罗格列酮与对照治疗之间广泛的对数优势比估计值，基于 95%置信（或可信）区间，对其统计显著性得出了混合结论。
在罕见事件情况下，传统荟萃分析方法获得的治疗效果估计可能存在偏差，并且覆盖率概率较差。当数据具有较大的研究间异质性时，这种趋势会恶化。一般来说，我们建议首先估计研究间治疗特异性风险的摘要，然后根据摘要适当地估计相对治疗效果的方法。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著差异。
