FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings.
CoRR(2023)
摘要
External control arms (ECA) can inform the early clinical development of
experimental drugs and provide efficacy evidence for regulatory approval in
non-randomized settings. However, the main challenge of implementing ECA lies
in accessing real-world data or historical clinical trials. Indeed, data
sharing is often not feasible due to privacy considerations related to data
leaving the original collection centers, along with pharmaceutical companies'
competitive motives. In this paper, we leverage a privacy-enhancing technology
called 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 the
implementation of ECA by limiting patients' data exposure. We show with
extensive experiments that FedECA outperforms its closest competitor,
matching-adjusted indirect comparison (MAIC), in terms of statistical power and
ability to balance the treatment and control groups. To encourage the use of
such methods, we publicly release our code which relies on Substra, an
open-source FL software with proven experience in privacy-sensitive contexts.
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