Bias, Precision and Timeliness of Historical (Background) Rate Comparison Methods for Vaccine Safety Monitoring: An Empirical Multi-Database Analysis.

Using real-world data and past vaccination data, we conducted a large-scale experiment to quantify bias, precision and timeliness of different study designs to estimate historical background (expected) compared to post-vaccination (observed) rates of safety events for several vaccines. We used negative (not causally related) and positive control outcomes. The latter were synthetically generated true safety signals with incident rate ratios ranging from 1.5 to 4. Observed vs. expected analysis using within-database historical background rates is a sensitive but unspecific method for the identification of potential vaccine safety signals. Despite good discrimination, most analyses showed a tendency to overestimate risks, with 20%-100% type 1 error, but low (0% to 20%) type 2 error in the large databases included in our study. Efforts to improve the comparability of background and post-vaccine rates, including age-sex adjustment and anchoring background rates around a visit, reduced type 1 error and improved precision but residual systematic error persisted. Additionally, empirical calibration dramatically reduced type 1 to nominal but came at the cost of increasing type 2 error.
Competing Interests: DP's research group has received grants for unrelated work from Amgen, Chiesi-Taylor, and UCB Biopharma SRL; and his department has received speaker/consultancy fees from Amgen, Astra-Zeneca, Astellas, Janssen, and UCB Bioparhma SRL. Authors PR and MJS were employed by Janssen R&D. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2021 Li, Lai, Ostropolets, Arshad, Tan, Casajust, Alshammari, Duarte-Salles, Minty, Areia, Pratt, Ryan, Hripcsak, Suchard, Schuemie and Prieto-Alhambra.)