August 13, 2025
Nature Communications

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

Cancer
FL
Biology
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.

Authors
Jean 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
Jerome Cros
Daniel Gonzalez
Robert Carreras-Torres
Adelaida Garcia Velasco
Kawther Abdilleh
Sudheer Doss
Felix Balazard
Mathieu Andreux