abc9acc6b5fdb9f7478d4fbefe8ffdcf kwae172.pdf a4cf41acd36d3b9ec1ad3ecbd8e32f8362af887a kwae172.pdf 38033277c3c089f9a044d8bafb09419674774b7a039628efea9d9f39471775e7 kwae172.pdf Title: 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 Subject: AcademicSubjects/MED00860, DOI: 10.1093/aje/kwae172, American Journal of Epidemiology, 194, 1, 3 7 2024. 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 September 1, 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, as the NCO, otitis externa. 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: (1) baseline probability of the confounder was higher in the control window and (2) 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 that even the strongest literature-reported confounder (COVID-19; RR for myocarditis€= 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. This article is part of a Special Collection on Pharmacoepidemiology. 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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 September 1, 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, as the NCO, otitis externa. 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: (1) baseline probability of the confounder was higher in the control window and (2) 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 that even the strongest literature-reported confounder (COVID-19; RR for myocarditis€= 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. This article is part of a Special Collection on Pharmacoepidemiology. Keywords: Creator: LaTeX with hyperref package Producer: Acrobat Distiller 24.0 (Windows) CreationDate: Wed Jan 08 05:43:42 CET 2025 ModDate: Wed Jan 08 05:43:42 CET 2025 ID: 0xb82aad3acee9349bb86c762a2fc80082, 0x1eb2ba225b171ae50d898254bd15364a Filters: FilterPipeline: FlateDecode Fonts: Type1: Font: BaseFont: MFNELK+MTMI FontSubset: true FirstChar: 40 LastChar: 62 FontDescriptor: FontName: MFNELK+MTMI Flags: Serif, Symbolic, Italic FontBBox: 0, -213, 987, 680 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: MFMPBE+CaeciliaLTStd-Bold FontSubset: true FirstChar: 37 LastChar: 121 FontDescriptor: FontName: MFMPBE+CaeciliaLTStd-Bold Flags: Serif, Symbolic, ForceBold FontBBox: -166, -273, 1141, 938 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: MFMPBF+CaeciliaLTStd-Roman FontSubset: true FirstChar: 37 LastChar: 237 FontDescriptor: FontName: MFMPBF+CaeciliaLTStd-Roman Flags: Serif, Symbolic FontBBox: -167, -270, 1136, 926 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: MFMPLD+CaeciliaLTStd-Italic FontSubset: true FirstChar: 65 LastChar: 243 FontDescriptor: FontName: MFMPLD+CaeciliaLTStd-Italic Flags: Serif, Symbolic, Italic FontBBox: -166, -270, 1053, 915 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: MFNBHJ+CaeciliaLTStd-Light FontSubset: true FirstChar: 33 LastChar: 252 FontDescriptor: FontName: MFNBHJ+CaeciliaLTStd-Light Flags: Serif, Symbolic FontBBox: -164, -266, 1130, 908 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: MFNCCG+CaeciliaLTStd-Heavy FontSubset: true FirstChar: 46 LastChar: 124 FontDescriptor: FontName: MFNCCG+CaeciliaLTStd-Heavy Flags: Serif, Symbolic, ForceBold FontBBox: -170, -277, 1146, 970 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: MFNCLE+CaeciliaLTStd-LightItalic FontSubset: true FirstChar: 37 LastChar: 243 FontDescriptor: FontName: MFNCLE+CaeciliaLTStd-LightItalic Flags: Serif, Symbolic, Italic FontBBox: -166, -267, 1033, 909 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: MFNCCH+MTSYN FontSubset: true FirstChar: 61 LastChar: 215 FontDescriptor: FontName: MFNCCH+MTSYN Flags: Symbolic FontBBox: 0, -954, 1043, 900 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true Font: BaseFont: MFNELJ+CaeciliaLTStd-BoldItalic FontSubset: true FirstChar: 110 LastChar: 110 FontDescriptor: FontName: MFNELJ+CaeciliaLTStd-BoldItalic Flags: Serif, Symbolic, Italic, ForceBold FontBBox: -169, -273, 1082, 932 FontFile3: true EncodingDictionary: Differences: true ToUnicode: true XMP: application/pdf 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 Bots Sophie H.Belitser SvetlanaGroenwold Rolf H. H.Durán Carlos E.Riera-Arnau JuditSchultze AnnaMessina DavideSegundo ElenaDouglas IanCarreras Juan JoséGarcia-Poza PatriciaGini RosaHuerta ConsueloMartín-Pérez MarMartin IvonnePaoletti OlgaBissacco Carlo AlbertoCorrecher-Martínez ElisaSouverein PatrickUrchueguía-Fornes ArantxaVillalobos FelipeSturkenboom Miriam C. J. M.Klungel Olaf H. AcademicSubjects/MED00860, DOI: 10.1093/aje/kwae172, American Journal of Epidemiology, 194, 1, 3 7 2024. 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 September 1, 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, as the NCO, otitis externa. 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: (1) baseline probability of the confounder was higher in the control window and (2) 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 that even the strongest literature-reported confounder (COVID-19; RR for myocarditis = 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. This article is part of a Special Collection on Pharmacoepidemiology. pharmacoepidemiologynegative controlsquantitative bias analysisCOVID-19 vaccine safetyself-controlled risk interval design © The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. 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