The Risk of Bias in Vaccine Effectiveness (RoB-VE) project: introduction to a methodological initiative to improve risk-of-bias assessment and reporting in vaccine effectiveness research
Notice bibliographique
Résumé
BACKGROUND AND OBJECTIVE: Vaccine effectiveness (VE) studies are essential for informing immunization policy and public health decision-making. However, the observational nature of most VE studies introduces unique methodological challenges, including biases that are not adequately addressed by existing risk-of-bias (RoB) tools. The Risk of Bias in Vaccine Effectiveness (RoB-VE) project is an international, multiphase methodological research initiative aimed at improving the quality, transparency, interpretability, and reporting of VE research. DISCUSSION: Funded by the Canadian Institutes of Health Research and supported by many global partners, the project seeks to generate a comprehensive toolkit for VE studies. This includes an RoB assessment resource tailored to VE study designs and a complementary reporting guideline to enhance consistency in VE study reporting. The project follows an evidence-informed approach, beginning with a review of the literature to inform tool development, and progressing through interest holder engagement, modified Delphi consensus, usability testing, and beta validation. This introductory paper outlines the rationale, scope, and methodology of the RoB-VE project. These efforts aim to strengthen the methodological foundation of VE research and support more reliable evidence synthesis and policy development. PLAIN LANGUAGE SUMMARY: VE studies measure how well vaccines work in real-world scenarios. These studies are essential for shaping vaccination recommendations. To assess the validity of VE studies, it is necessary to carry out an RoB assessment, which involves looking at different aspects of the study (eg, data collection methods, how participants are recruited, etc.) that have the potential to yield misleading results. Existing RoB assessment tools do not fully capture issues particularly relevant to VE studies and inconsistent reporting limits their usefulness. To address this, we are conducting the RoB-VE project. This project aims to improve the quality, transparency, interpretability, and reporting of VE research through the development, validation, and dissemination of a robust and user-friendly RoB assessment tool, specifically tailored for assessing VE studies. Our methodology involves a comprehensive multistep process based on established approaches. A broad range of international participants with diverse expertise and profiles will be engaged along the way to refine and finalize the tool. After pilot testing the beta version of the tool and making further refinements, we aim to deliver version 1 of the tool, which will undergo a large-scale application phase to assess its reliability and usefulness. Additionally, we will develop a reporting guideline to enhance the completeness of reporting of VE studies. This introductory paper outlines the rationale, scope, and methodology of the RoB-VE project. This project will elevate the standards of evidence synthesis, ultimately contributing to more reliable, transparent, and impactful research in the critical field of VE evaluation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,793 | 0,832 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,005 |
| Méta-épidémiologie (sens large) | 0,012 | 0,020 |
| Bibliométrie | 0,009 | 0,007 |
| Études des sciences et des technologies | 0,003 | 0,010 |
| Communication savante | 0,011 | 0,010 |
| Science ouverte | 0,009 | 0,018 |
| Intégrité de la recherche | 0,014 | 0,024 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».