Bots, Sophie H; Belitser, Svetlana; Groenwold, Rolf HH; Durán, Carlos E; Riera-Arnau, Judit; Schultze, Anna; Messina, Davide; Segundo, Elena; Douglas, Ian; Carreras, Juan José; +13 more... Garcia-Poza, Patricia; Gini, Rosa; Huerta, Consuelo; Martín-Pérez, Mar; Martin, Ivonne; Paoletti, Olga; Bissacco, Carlo Alberto; Correcher-Martínez, Elisa; Souverein, Patrick; Urchuequía, Arantxa; Villalobos, Felipe; Sturkenboom, Miriam CJM; Klungel, Olaf H; (2024) Applying two approaches to detect unmeasured confounding due to time-varying variables in a self-controlled risk interval design evaluating COVID-19 vaccine safety signals, using myocarditis as a case example. American journal of epidemiology. kwae172-. ISSN 0002-9262 DOI: https://doi.org/10.1093/aje/kwae172
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Abstract
We test the robustness of the self-controlled risk interval (SCRI) design in a setting where time between doses may introduce time-varying confounding, using both negative control outcomes (NCOs) and quantitative bias analysis (QBA). All vaccinated cases identified from 5 European databases between 1 September 2020 and end of data availability were included. Exposures were doses 1-3 of the Pfizer, Moderna, AstraZeneca, and Janssen COVID-19 vaccines; outcomes were myocarditis and otitis externa (NCO). The SCRI used a 60-day control window and dose-specific 28-day risk windows, stratified by vaccine brand and adjusted for calendar time. The QBA included two scenarios: (i) baseline probability of the confounder was higher in the control window and (ii) vice versa. The NCO was not associated with any of the COVID-19 vaccine types or doses except Moderna dose 1 (IRR = 1.09, 95%CI 1.01-1.09). The QBA suggested even the strongest literature-reported confounder (COVID-19; RRmyocarditis = 18.3) could only explain away part of the observed effect from IRR = 3 to IRR = 1.40. The SCRI seems robust to unmeasured confounding in the COVID-19 setting, although a strong unmeasured confounder could bias the observed effect upward. Replication of our findings for other safety signals would strengthen this conclusion.
Item Type | Article |
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Faculty and Department | Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology |
PubMed ID | 38960670 |
Elements ID | 226604 |
Official URL | http://dx.doi.org/10.1093/aje/kwae172 |
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Filename: Bots-etal-2024-Applying-two-approaches-to-detect-unmeasured-confounding-due-to-time-varying-variables.pdf
Licence: Creative Commons: Attribution 4.0
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