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

TL;DR

FedECA introduces a federated learning approach for causal inference with time-to-event data, enabling analysis across distributed datasets without data pooling, thus respecting privacy regulations and facilitating drug development.

Contribution

It develops a novel federated inverse probability of treatment weighting method for time-to-event outcomes in distributed data settings.

Findings

Successfully applied to real-world pancreatic cancer data

Enables causal inference without data sharing

Supports federated research networks

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.