AI Article Synopsis
- A large-scale study was conducted to evaluate the bias, precision, and timeliness of different designs in estimating vaccine safety events by comparing expected rates with observed rates post-vaccination.
- The analysis revealed that using historical background rates to identify potential safety signals for vaccines was sensitive but often overestimated risks, showing high type 1 error (20%-100%) and low type 2 error (0%-20%).
- Adjusting for factors like age and sex improved the accuracy but still left some systematic errors, while empirical calibration helped reduce type 1 error though at the expense of increasing type 2 error.
Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652333 | PMC |
| http://dx.doi.org/10.3389/fphar.2021.773875 | DOI Listing |
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