FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 31 of its 127 references that are in the map. First posted on arXiv 28 Nov 2023.
Builds on
31 of 127 references.
References · 31
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19 more
- 2020Federated Bayesian Optimization via Thompson Samplingcited by 77
- 2020FedDANE: A Federated Newton-Type Methodcited by 76
- 2021What's a good imputation to predict with missing values?cited by 62
- 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
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- 2021Distributed Second Order Methods with Fast Rates and Compressed Communicationcited by 25
- 2021Federated Causal Inference in Heterogeneous Observational Datacited by 24
- 2020Federated Survival Analysis with Discrete-Time Cox Modelscited by 19
- 2022Industry-Scale Orchestrated Federated Learning for Drug Discoverycited by 19
- 2023Randomized Quantization is All You Need for Differential Privacy in Federated Learningcited by 17
- 2021Federated Adaptive Causal Estimation (FACE) of Target Treatment Effectscited by 16
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- 2019Doubly robust treatment effect estimation with missing attributescited by 12
- 2022Collaborative causal inference on distributed datacited by 10
- 2022SecureFedYJ: a safe feature Gaussianization protocol for Federated Learningcited by 9
- 2023Federated Survival Forestscited by 8
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2 papers.