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

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.

External Control Arm methods for clinical trials were developed to compare the efficacy of a treatment to a control group that is built with data from external sources. Here, the authors present FedECA, a privacy-enhancing method for analyzing treatment effects across institutions, streamlining multi-centric trial design and thereby accelerating drug development while minimizing patient data exposure.