Evidence map›Paper›PMID 38960670›Full record

ArticleAmerican journal of epidemiology2025

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.

Sophie H Bots, Svetlana Belitser, Rolf H H Groenwold, Carlos E Durán, Judit Riera-Arnau, Anna Schultze, Davide Messina, Elena Segundo, Ian Douglas, Juan José Carreras and 13 more

Abstract read
In one paragraph

Article in American journal of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Core Concepts: Self-Controlled Designs in Pharmacoepidemiology.Pharmacoepidemiology and drug safety · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Sophie H BotsDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3508 TB, Utrecht, The Netherlands.ORCID 0000-0002-4483-5582
Svetlana BelitserDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3508 TB, Utrecht, The Netherlands.
Rolf H H GroenwoldDepartment of Clinical Epidemiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.
Carlos E DuránDepartment of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Health, University Medical Center Utrecht, 3584 CG, Utrecht, The Netherlands.
Judit Riera-ArnauDepartment of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Health, University Medical Center Utrecht, 3584 CG, Utrecht, The Netherlands.
Anna SchultzeFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, WC1E 7HT, London, UK.
Davide MessinaAgenzia Regionale di Sanità, 50141, Florence, Toscana, Italy.
Elena SegundoFundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007, Barcelona, Spain.
Ian DouglasFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, WC1E 7HT, London, UK.
Juan José CarrerasVaccine Research Department, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO - Public Health), 46020, Valencia, Spain.
Patricia Garcia-PozaSpanish Agency for Medicines and Medical Devices (AEMPS), 28022, Madrid, Spain.
Rosa GiniAgenzia Regionale di Sanità, 50141, Florence, Toscana, Italy.
Consuelo HuertaSpanish Agency for Medicines and Medical Devices (AEMPS), 28022, Madrid, Spain.
Mar Martín-PérezSpanish Agency for Medicines and Medical Devices (AEMPS), 28022, Madrid, Spain.
Ivonne MartinDepartment of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Health, University Medical Center Utrecht, 3584 CG, Utrecht, The Netherlands.
Olga PaolettiAgenzia Regionale di Sanità, 50141, Florence, Toscana, Italy.
Carlo Alberto BissaccoFundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007, Barcelona, Spain.
Elisa Correcher-MartínezVaccine Research Department, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO - Public Health), 46020, Valencia, Spain.
Patrick SouvereinDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3508 TB, Utrecht, The Netherlands.
Arantxa Urchueguía-FornesVaccine Research Department, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO - Public Health), 46020, Valencia, Spain.
Felipe VillalobosFundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007, Barcelona, Spain.
Miriam C J M SturkenboomDepartment of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Health, University Medical Center Utrecht, 3584 CG, Utrecht, The Netherlands.
Olaf H KlungelDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3508 TB, Utrecht, The Netherlands.

Funding

European Medicines Agency
6 · The paper itself

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.

Indexed as

COVID-19COVID-19 VaccinesMyocarditisAdultAgedConfounding Factors, EpidemiologicEuropeFemaleHumansMaleMiddle AgedSARS-CoV-2Time FactorsCOVID-19 VaccinesCOVID-19 vaccine safetynegative controlspharmacoepidemiologyquantitative bias analysisself-controlled risk interval design

Identifiers

PMID38960670
PMCPMC11735966

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.