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

Abstract

Introduction: 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.

Methods: A 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.

Results: The 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.

Conclusions: The 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.

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

Fig. 1

Visual representation of the proposed methodological framework

Fig. 2

Overview of the executed steps and produced research objects during the implementation of the proposed methodological approach, step 1 to 4, preparing for the assessment of the real-world effectiveness of a primary vaccination schedule as compared to partial or no vaccination in preventing SARS-CoV-2 infection, in populations spanning national borders

Fig. 3

The causal model (using a DAG), Common Data Model (CDM) and synthetic data, and how they relate to each other. The DAG, capturing assumptions on factors and relationships when assessing the real-world effectiveness of a primary vaccination schedule as compared to partial or no vaccination in preventing SARS-CoV-2 infection in populations spanning national borders, is visualized. The structure of the CDM and synthetic data, as constructed based on the drafted causal model, is presented

Fig. 4

Graphical overview of the developed analytical pipeline, consisting of different subsequent modules, each producing an interactive report. Implementation of step 5 of the proposed methodological approach to assess the real-world effectiveness of a primary vaccination schedule as compared to partial or no vaccination in preventing SARS-CoV-2 infection, in populations spanning national borders