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CC BY 4.0 · Appl Clin Inform 2025; 16(05): 1507-1517
DOI: 10.1055/a-2710-4226
State of the Art/Best Practice Paper

Best Practices to Design, Plan, and Execute Large-Scale Federated Analyses—Key Learnings and Suggestions from a Study Comprising 52 Databases

Authors

  • Theresa Burkard

    1   Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom

  • Montse Camprubi

    2   Synapse, Carrer de la Diputació, Barcelona, Cataluña, Spain

  • Daniel Prieto-Alhambra

    1   Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
    3   Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, The Netherlands

  • Peter Rijnbeek

    3   Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, The Netherlands

  • Marta Pineda-Moncusi

    1   Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom
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Abstract

Background

Federated network studies allow data to remain locally while the research is conducted through the sharing of analytical code and aggregated results across different health care settings and countries. A large number of databases have been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), boosting the use of analytical pipelines for standardized observational research within this open science framework. Transparency, reproducibility, and robustness of results have positioned federated analyses using the OMOP CDM within the European Health Data and Evidence Network (EHDEN) as an essential tool for generating large-scale evidence.

Objectives

We conducted large-scale federated analyses involving 52 databases from 19 countries using the OMOP CDM. In this State-of-the-Art/Best Practice article, we aimed to share key lessons and strategies for conducting such complex, large multidatabase analyses.

Results

Meticulous planning, establishing a strong community of collaborators, efficient communication channels, standardized analytics, and strategic division of responsibilities are essential. We highlight the benefits of network engagement, cross-fertilization of ideas, and shared learning. Further key elements contributing to the study's success included an inclusive, incremental implementation of the analytical code, timely engagement of data partners, and community webinars to discuss and interpret study findings.

Conclusion

We received predominantly positive feedback from data custodians about their participation, and included input for further improvements for future large-scale federated network studies from this shared learning experience.

Keywords

OMOP - federated analyses - best practice - OHDSI - EHDEN

Protection of Human and Animal Subjects

This article suggests best practices for large-scale multinational federated network studies and therefore does not fall under human subject research, which means that no ethical approval was required for this publication. Ethical approval was obtained for the discussed research underlying the best practices recommendations.




Publication History

Received: 23 December 2024

Accepted: 24 August 2025

Accepted Manuscript online:
26 September 2025

Article published online:
30 October 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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