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Activity Number: 222 - Cross-Disciplinary Research on Health Data Science
Type: Invited
Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
Sponsor: National Institute of Statistical Sciences
Abstract #319260
Title: Vaccine Safety Surveillance Using Routinely Collected Health Care Data: An Empirical Evaluation of Epidemiological Designs
Author(s): Marc A. Suchard* and Martijn J. Schuemie
Companies: UCLA and Janssen Research and Development
Keywords: EHR; Vaccine safety; Epidemiological design; Negative controls; Empirical calibration
Abstract:

Routinely collected healthcare data complement clinical trials when ensuring the safety of vaccines, but uncertainty remains about what epidemiological design to use. Using 3 administrative claims and 1 EHR database, we evaluate the case-control, comparative cohort, historical comparator, and self-controlled designs against historical vaccinations with real negative control outcomes and simulated positive controls. Most methods show large type 1 error. Cohort methods appear either positively or negatively biased, depending on the choice of comparator index date. Empirical calibration using negative control outcomes can restore type 1 error to close to nominal. After calibration, the self-controlled case series shows the shortest time to detection. When applying any method for vaccine safety surveillance we recommend considering the potential for systematic error, especially due to confounding, which for many designs appears to be substantial. Adjusting for age and sex alone is likely not sufficient to address the differences between vaccinated and unvaccinated, and for the cohort method the choice of index date plays an important role in the comparability of the groups.


Authors who are presenting talks have a * after their name.

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