  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.
