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

BMC Medical Research Methodology

Research

Simon Saldner¹  Francisco Estupiñán-Romero²  Enrique Bernal-Delgado²  Santiago Royo-Sierra²  Natalia Martínez-Lizaga²  Javier González-Galindo²  Stian Soiland-Reyes³  Nina Van Goethem⁴  Marjan Meurisse⁵  Alexander Degelsegger-Marquez⁶  Lorenz Dolanski-Aghamanoukjan⁶ 

[1] Data Archiving and Networked Services, Royal Netherlands Academy of Arts & Sciences, Amsterdam, The Netherlands;Data science for Health Services and Policy, Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain;Department of Computer Science, The University of Manchester, Manchester, UK;Informatics Institute, Universiteit van Amsterdam, Amsterdam, The Netherlands;Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium;Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium;IREC – EPID, Université Catholique de Louvain, Brussels, Belgium;International Affairs, Policy, Evaluation and Digitalisation, Gesundheit Österreich GmbH (GÖG), Vienna, Austria;

关键词: Federated analysis;    Causal inference;    Real-world data;    Comparative effectiveness;    Vaccines;    COVID-19;    Pandemic preparedness;   

DOI  :  10.1186/s12874-023-02068-3

 received in 2023-07-26, accepted in 2023-10-11,  发布年份 2023

来源: Springer

【 摘 要 】

IntroductionCausal 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.MethodsA framework for approaching federated causal inference by re-using routinely collected observational data across different regions, based on principles of legal, organizational, semantic and technical interoperability, is proposed. The framework includes step-by-step guidance, from defining a research question, to establishing a causal model, identifying and specifying data requirements in a common data model, generating synthetic data, and developing an interoperable and reproducible analytical pipeline for distributed deployment. The conceptual and instrumental phase of the framework was demonstrated and an analytical pipeline implementing federated causal inference was prototyped using open-source software in preparation for the assessment of real-world effectiveness of SARS-CoV-2 primary vaccination in preventing infection in populations spanning different countries, integrating a data quality assessment, imputation of missing values, matching of exposed to unexposed individuals based on confounders identified in the causal model and a survival analysis within the matched population.ResultsThe conceptual and instrumental phase of the proposed methodological framework was successfully demonstrated within the BY-COVID project. Different Findable, Accessible, Interoperable and Reusable (FAIR) research objects were produced, such as a study protocol, a data management plan, a common data model, a synthetic dataset and an interoperable analytical pipeline.ConclusionsThe framework provides a systematic approach to address federated cross-national policy-relevant causal research questions based on sensitive population, health and care data in a privacy-preserving and interoperable way. The methodology and derived research objects can be re-used and contribute to pandemic preparedness.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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