FedECA: A Federated External Control Arm Method 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, Jérome Cros, Daniel Gonzalez, Robert Carreras-Torres, Adelaida Garcia Velasco, Kawther Abdilleh, Sudheer Doss, Félix Balazard, Mathieu Andreux
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/owkin/fedecaOfficialIn paperpytorch★ 15
Abstract
External control arms (ECA) can inform the early clinical development of experimental drugs and provide efficacy evidence for regulatory approval. However, the main challenge in implementing ECA lies in accessing real-world or historical clinical trials data. 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 new method, 'FedECA' that leverages federated learning (FL) to enable inverse probability of treatment weighting (IPTW) for time-to-event outcomes on separate cohorts without needing to pool data. To showcase the potential of FedECA, we apply it in different settings of increasing complexity culminating with a real-world use-case in which FedECA 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 FedECA will foster the creation of federated research networks and thus accelerate drug development.