2023/05/19 by Fan Bu, Bu, Fan, Martijn J. Schuemie +17 · 1 citation
Mathematics · Medicine · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Influenza Virus Research Studies #Methodology (stat.ME) #Statistical Methods in Clinical Trials #Vaccine Coverage and Hesitancy
paper · pdf · doi:10.48550/arxiv.2305.12034
openalex publication_date 2023/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.