Department of Biostatistics, University of California, Los Angeles, California, USA.
Department of Biostatistics, University of Michigan-Ann Arbor, Ann Arbor, Michigan, USA.
Stat Med. 2024 Jan 30;43(2):395-418. doi: 10.1002/sim.9968. Epub 2023 Nov 27.
Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self-controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open-source R package EvidenceSynthesis.
上市后安全性监测是大规模疫苗接种计划的一个组成部分。通常依赖于随着时间的推移实时健康数据的序贯分析，安全性监测受到序贯多重检验和观察数据中残留混杂引起的偏差的挑战。目前基于最大化序贯概率比检验（MaxSPRT）的标准方法不能令人满意地解决这些实际挑战，它仍然是一个需要预先规定监测计划的刚性框架。我们开发了一种替代的贝叶斯监测程序，该程序使用更灵活的框架解决了上述两个挑战。为了减轻偏差，我们共同分析了一组大型的阴性对照结果，这些结果是与疫苗没有已知关联的不良事件，以便为经验偏差分布提供信息，然后我们通过贝叶斯层次模型将其纳入估计疫苗暴露对感兴趣的不良事件的影响。为了解决多重检验和提高灵活性，在每次分析时间点，我们更新支持替代假设的后验概率，即疫苗接种会增加不良事件的风险，然后使用它来进行安全性信号的序贯检测。通过使用涵盖超过 3.6 亿患者的六个美国观察性医疗保健数据库进行实证评估，我们针对两种流行病学设计（历史对照和自身对照病例系列），在检验误差和估计准确性方面将提出的程序与 MaxSPRT 进行基准测试。我们证明，我们的程序大大降低了第一类错误率，保持了高统计功效和快速信号检测，并提供了比 MaxSPRT 更准确的估计。鉴于实证研究的广泛程度，产生了超过 700 万组结果，我们在公共 R ShinyApp 中展示了所有结果。作为促进开放科学的努力，我们在开源 R 包 EvidenceSynthesis 中提供了我们方法的完整实现。
