FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings
This work addresses data access and privacy issues in clinical research for drug developers and regulators, offering a practical solution but is incremental as it adapts existing federated learning to a specific causal inference task.
The paper tackles the challenge of accessing sufficient real-world clinical data for causal inference in drug development by developing a federated learning method that enables inverse probability of treatment weighting for time-to-event outcomes without pooling data, applying it to compare chemotherapy regimens in metastatic pancreatic cancer using three separate cohorts.
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.