Early evidence for the safety of certain COVID-19 vaccines using empirical Bayesian modeling from VAERS

The advent of vaccines against SARS-CoV-2 ushered in an unprecedented global response to COVID-19, with the largest and most ambitious mass vaccination campaign in human history. The scale of this effort means that safety signals suggesting adverse effects may only be detectable using passive reporting. This paper examines reports to the CDC/FDA's VAERS system in the first six months of 2021, using an empirical Bayesian model with a gamma Poisson shrinker to identify potential safety signals from COVID-19 vaccines currently on the U.S. market. Based on this preliminary data, it is concluded that the COVID-19 vaccine's safety significantly exceeds that of previously marketed vaccines, and other than a known risk of thrombotic events, no safety signals of concern emerge.

Class-imbalanced subsampling lasso algorithm for discovering adverse drug reactions

Background All methods routinely used to generate safety signals from pharmacovigilance databases rely on disproportionality analyses of counts aggregating patients’ spontaneous reports. Recently, it was proposed to analyze individual spontaneous reports directly using Bayesian lasso logistic regressions. Nevertheless, this raises the issue of choosing an adequate regularization parameter in a variable selection framework while accounting for computational constraints due to the high dimension of the data. Purpose Our main objective is to propose a method, which exploits the subsampling idea from Stability Selection, a variable selection procedure combining subsampling with a high-dimensional selection algorithm, and adapts it to the specificities of the spontaneous reporting data, the latter being characterized by their large size, their binary nature and their sparsity. Materials and method Given the large imbalance existing between the presence and absence of a given adverse event, we propose an alternative subsampling scheme to that of Stability Selection resulting in an over-representation of the minority class and a drastic reduction in the number of observations in each subsample. Simulations are used to help define the detection threshold as regards the average proportion of false signals. They are also used to compare the performances of the proposed sampling scheme with that originally proposed for Stability Selection. Finally, we compare the proposed method to the gamma Poisson shrinker, a disproportionality method, and to a lasso logistic regression approach through an empirical study conducted on the French national pharmacovigilance database and two sets of reference signals. Results Simulations show that the proposed sampling strategy performs better in terms of false discoveries and is faster than the equiprobable sampling of Stability Selection. The empirical evaluation illustrates the better performances of the proposed method compared with gamma Poisson shrinker and the lasso in terms of number of reference signals retrieved.

Endotoxin-Like Reactions With Intravenous Gentamicin Results From Pharmacovigilance Tools Under Investigation

Objective:To apply two data mining algorithms (DMAs) to Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) reports that involved endotoxin-like reactions with intravenous gentamicin to determine whether a signal of disproportionate reporting of these events would have been generated concurrently with surveillance based on clinical observation.Design:Multi-item gamma-Poisson shrinker (MGPS) and proportional reporting ratios (PRRs) were used. Data used for data mining consisted of an extract of the FDA AERS database. Previously published details of clusters of endotoxin-like reactions to intravenous gentamicin were used to select adverse events for data mining.Results:The first signal of disproportionate reporting with any relevant event occurred in 1998, the year in which the outbreak was identified and evaluated by the Centers for Disease Control and Prevention and the FDA. In 1997, there were only 6 reports of rigors in the AERS; this jumped to 68 in 1998. In 1998, a signal was generated for endotoxic shock with PRRs but not with MGPS, based on one case.Conclusions:The two DMAs generated signals concurrently with the influx of reports. It would have been difficult for safety reviewers to ignore an increase in rigors by traditional methods of safety surveillance; therefore, DMAs might not have had a great deal to offer in this instance. If data mining were considered as a second-line defense to diligent clinical observations under similar circumstances, simple disproportionality methods such as PRRs might be more useful than DMAs such as MGPS when commonly cited thresholds are used.