FedECA: federated external control arms for causal inference with time-to-event data in distributed settings

Jean Ogier du Terrail (), Quentin Klopfenstein, Honghao Li, Imke Mayer, Nicolas Loiseau, Mohammad Hallal, Michael Debouver, Thibault Camalon, Thibault Fouqueray, Jorge Arellano Castro, Zahia Yanes, Laëtitia Dahan, Julien Taïeb, Pierre Laurent-Puig, Jean-Baptiste Bachet, Shulin Zhao, Remy Nicolle, Jérôme Cros, Daniel Gonzalez, Robert Carreras-Torres, Adelaida Garcia Velasco, Kawther Abdilleh, Sudheer Doss, Félix Balazard and Mathieu Andreux
Additional contact information
Jean Ogier du Terrail: Inc.
Quentin Klopfenstein: Inc.
Honghao Li: Inc.
Imke Mayer: Inc.
Nicolas Loiseau: Inc.
Mohammad Hallal: Inc.
Michael Debouver: Inc.
Thibault Camalon: Inc.
Thibault Fouqueray: Inc.
Jorge Arellano Castro: Inc.
Zahia Yanes: Inc.
Laëtitia Dahan: Hôpital la Timone
Julien Taïeb: Université Paris Cité
Pierre Laurent-Puig: Sorbonne Université, Inserm, Université Paris Cité
Jean-Baptiste Bachet: APHP
Shulin Zhao: Sorbonne Université, Inserm, Université Paris Cité
Remy Nicolle: CNRS
Jérôme Cros: Université Paris Cité - FHU MOSAIC, Beaujon Hospital
Daniel Gonzalez: Fédération Francophone de Cancérologie Digestive
Robert Carreras-Torres: Institut d’Investigació Biomèdica de Girona (IDIBGI)
Adelaida Garcia Velasco: Institut d’Investigació Biomèdica de Girona (IDIBGI)
Kawther Abdilleh: Pancreatic Cancer Action Network
Sudheer Doss: Pancreatic Cancer Action Network
Félix Balazard: Inc.
Mathieu Andreux: Inc.

Nature Communications, 2025, vol. 16, issue 1, 1-22

Abstract: Abstract External control arms can inform early clinical development of experimental drugs and provide efficacy evidence for regulatory approval. However, accessing sufficient real-world or historical clinical trials data is challenging. Indeed, regulations protecting patients’ rights by strictly controlling data processing make pooling data from multiple sources in a central server often difficult. To address these limitations, we develop a method that leverages federated learning to enable inverse probability of treatment weighting for time-to-event outcomes on separate cohorts without needing to pool data. To showcase its potential, we apply it in different settings of increasing complexity, culminating with a real-world use-case in which our method is used to compare the treatment effect of two approved chemotherapy regimens using data from three separate cohorts of patients with metastatic pancreatic cancer. By sharing our code, we hope it will foster the creation of federated research networks and thus accelerate drug development.

Date: 2025
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DOI: 10.1038/s41467-025-62525-z

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