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External control arms (ECA) can inform the early clinical development ofexperimental drugs and provide efficacy evidence for regulatory approval innon-randomized settings. However, the main challenge of implementing ECA liesin accessing real-world data or historical clinical trials. Indeed, datasharing is often not feasible due to privacy considerations related to dataleaving the original collection centers, along with pharmaceutical companies'competitive motives. In this paper, we leverage a privacy-enhancing technologycalled federated learning (FL) to remove some of the barriers to data sharing.We introduce a federated learning inverse probability of treatment weighted(IPTW) method for time-to-event outcomes called FedECA which eases theimplementation of ECA by limiting patients' data exposure. We show withextensive experiments that FedECA outperforms its closest competitor,matching-adjusted indirect comparison (MAIC), in terms of statistical power andability to balance the treatment and control groups. To encourage the use ofsuch methods, we publicly release our code which relies on Substra, anopen-source FL software with proven experience in privacy-sensitive contexts.

FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings