Framework for federated causal inference based on real-world observational data sources (BY-COVID)

Abstract Background Causal inference techniques help researchers and policy-makers to evaluate public health interventions. Approaching causal inference by re-using routinely collected observational data across different regions 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 estimate causal effects in a federated research infrastructure is showcased in the baseline use case of the European BY-COVID project. Methods A framework, implementing existing methodologies and expertise, is proposed, enabling federated research based on routinely collected sensitive individual-level data. The framework includes step-by-step guidelines, from defining a research question, to establishing a causal model, identifying and specifying data requirements in a data model, and developing an interoperable and reproducible analytical pipeline for distributed deployment. Using open-source software, a complete workflow implementing federated causal inference was prototyped to assess the 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 based on identified confounders and a survival analysis within the matched population. Results The proposed methodological framework was successfully demonstrated within the BY-COVID use case, illustrating its feasibility and value. Different Findable, Accessible, Interoperable and Reusable (FAIR) research objects were produced, such as a study protocol, a data management plan, data model and interoperable analytical pipeline. Conclusions The framework provides a systematic approach to address policy-relevant causal research questions in a privacy-preserving way. The methodology and derived research objects can be re-used and contribute to pandemic preparedness. Key messages • Estimating causal effects using observational data in a privacy-preserving way was demonstrated to be achievable and can help policy-makers to evaluate public health interventions. • The methodology, constituted of Findable, Accessible, Interoperable and Reusable (FAIR) research objects, is generalizable to other research questions and contributes to pandemic preparedness.