Post-market 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 the difficulty of sequential multiple testing and by biases induced by residual confounding. 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 pre-specification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both challenges using a more flexible framework. We adopt a joint statistical modeling approach to sequentially estimate the effect of vaccine exposure on the adverse event of interest and correct for estimation bias by simultaneously analyzing a large set of negative control outcomes through a Bayesian hierarchical model. We then compute a posterior probability of the alternative hypothesis via Markov chain Monte Carlo sampling and 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, delivers fast signal detection, and provides considerably more accurate estimation. As an effort to promote open science, we present all empirical results in an R ShinyApp and provide full implementation of our method in the R package EvidenceSynthesis.

翻译：上市后安全性监测是大规模疫苗接种计划的核心组成部分。由于通常依赖于对实时累积的真实世界健康数据进行序贯分析，安全性监测面临序贯多重检验的困难以及残余混杂导致的偏倚两大挑战。当前基于最大化序贯概率比检验（MaxSPRT）的标准方法未能令人满意地解决这些实际问题，且作为一种刚性框架，其要求预先指定监测计划。我们开发了一种替代性贝叶斯监测程序，通过更灵活的框架同时应对上述两大挑战。我们采用联合统计建模方法，序贯估计疫苗暴露对目标不良事件的影响，并通过贝叶斯层次模型同步分析大量阴性对照结局以校正估计偏倚。随后通过马尔可夫链蒙特卡洛采样计算备择假设的后验概率，并将其用于安全性信号的序贯检测。基于覆盖超3.6亿患者的六个美国观察性医疗保健数据库的实证评估，我们分别在历史对照与自身对照病例系列两种流行病学设计下，将该程序与MaxSPRT在检验误差与估计精度方面进行了基准对比。结果表明，我们的程序显著降低了I类错误率，保持了高统计功效，实现了快速信号检测，并提供了更精确的估计。为促进开放科学，我们将所有实证结果呈现在R ShinyApp中，并在R包EvidenceSynthesis中提供方法的完整实现。