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

doi:10.1186/s12874-023-02068-3

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

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Research output: Contribution to journal/periodical › Article › Scientific › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Number of pages	15
Journal	BMC Medical Research Methodology
Volume	23
Issue number	1
DOIs	https://doi.org/10.1186/s12874-023-02068-3
Publication status	Published - 23 Oct 2023

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10.1186/s12874-023-02068-3

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Meurisse, M., Estupiñán-Romero, F., González-Galindo, J., Martínez-Lizaga, N., Royo-Sierra, S., Saldner, S., Dolanski-Aghamanoukjan, L., Degelsegger-Marquez, A., Soiland-Reyes, S., Van Goethem, N., & Bernal-Delgado, E. (2023). Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment. BMC Medical Research Methodology, 23(1). https://doi.org/10.1186/s12874-023-02068-3

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

abstract = "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.",

author = "Marjan Meurisse and Francisco Estupi{\~n}{\'a}n-Romero and Javier Gonz{\'a}lez-Galindo and Natalia Mart{\'i}nez-Lizaga and Santiago Royo-Sierra and Simon Saldner and Lorenz Dolanski-Aghamanoukjan and Alexander Degelsegger-Marquez and Stian Soiland-Reyes and {Van Goethem}, Nina and Enrique Bernal-Delgado",

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doi = "10.1186/s12874-023-02068-3",

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Meurisse, M, Estupiñán-Romero, F, González-Galindo, J, Martínez-Lizaga, N, Royo-Sierra, S, Saldner, S, Dolanski-Aghamanoukjan, L, Degelsegger-Marquez, A, Soiland-Reyes, S, Van Goethem, N & Bernal-Delgado, E 2023, 'Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment', BMC Medical Research Methodology, vol. 23, no. 1. https://doi.org/10.1186/s12874-023-02068-3

Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment. / Meurisse, Marjan; Estupiñán-Romero, Francisco; González-Galindo, Javier et al.
In: BMC Medical Research Methodology, Vol. 23, No. 1, 23.10.2023.

Research output: Contribution to journal/periodical › Article › Scientific › peer-review

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AU - Meurisse, Marjan

AU - Estupiñán-Romero, Francisco

AU - González-Galindo, Javier

AU - Martínez-Lizaga, Natalia

AU - Royo-Sierra, Santiago

AU - Saldner, Simon

AU - Dolanski-Aghamanoukjan, Lorenz

AU - Degelsegger-Marquez, Alexander

AU - Soiland-Reyes, Stian

AU - Van Goethem, Nina

AU - Bernal-Delgado, Enrique

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AB - 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.

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DO - 10.1186/s12874-023-02068-3

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JO - BMC Medical Research Methodology

JF - BMC Medical Research Methodology

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

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