Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment

Marjan Meurisse, Francisco Estupiñán-Romero, Javier González-Galindo, Natalia Martínez-Lizaga, Santiago Royo-Sierra, Simon Saldner, Lorenz Dolanski-Aghamanoukjan, Alexander Degelsegger-Marquez, Stian Soiland-Reyes, Nina Van Goethem, Enrique Bernal-Delgado

Onderzoeksoutput: Bijdrage aan wetenschappelijk tijdschrift/periodieke uitgaveArtikelWetenschappelijkpeer review

Samenvatting

Causal inference helps researchers and policy-makers to evaluate public health interventions. When comparing interventions or public health programs by leveraging observational sensitive individual-level data from populations crossing jurisdictional borders, a federated approach (as opposed to a pooling data approach) can be used. Approaching causal inference by re-using routinely collected observational data across different regions in a federated manner, is challenging and guidance is currently lacking. With the aim of filling this gap and allowing a rapid response in the case of a next pandemic, a methodological framework to develop studies attempting causal inference using federated cross-national sensitive observational data, is described and showcased within the European BeYond-COVID project.
Originele taal-2Engels
Aantal pagina's15
TijdschriftBMC Medical Research Methodology
Volume23
Nummer van het tijdschrift1
DOI's
StatusGepubliceerd - 23 okt. 2023

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