FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| author | Terrail, Jean Ogier du Klopfenstein, Quentin Li, Honghao Mayer, Imke Loiseau, Nicolas Hallal, Mohammad Debouver, Michael Camalon, Thibault Fouqueray, Thibault Castro, Jorge Arellano Yanes, Zahia Dahan, Laëtitia Taïeb, Julien Laurent-Puig, Pierre Bachet, Jean-Baptiste Zhao, Shulin Nicolle, Remy Cros, Jérome Gonzalez, Daniel Carreras-Torres, Robert Velasco, Adelaida Garcia Abdilleh, Kawther Doss, Sudheer Balazard, Félix Andreux, Mathieu |
| author_facet | Terrail, Jean Ogier du Klopfenstein, Quentin Li, Honghao Mayer, Imke Loiseau, Nicolas Hallal, Mohammad Debouver, Michael Camalon, Thibault Fouqueray, Thibault Castro, Jorge Arellano Yanes, Zahia Dahan, Laëtitia Taïeb, Julien Laurent-Puig, Pierre Bachet, Jean-Baptiste Zhao, Shulin Nicolle, Remy Cros, Jérome Gonzalez, Daniel Carreras-Torres, Robert Velasco, Adelaida Garcia Abdilleh, Kawther Doss, Sudheer Balazard, Félix Andreux, Mathieu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_16984 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings Terrail, Jean Ogier du Klopfenstein, Quentin Li, Honghao Mayer, Imke Loiseau, Nicolas Hallal, Mohammad Debouver, Michael Camalon, Thibault Fouqueray, Thibault Castro, Jorge Arellano Yanes, Zahia Dahan, Laëtitia Taïeb, Julien Laurent-Puig, Pierre Bachet, Jean-Baptiste Zhao, Shulin Nicolle, Remy Cros, Jérome Gonzalez, Daniel Carreras-Torres, Robert Velasco, Adelaida Garcia Abdilleh, Kawther Doss, Sudheer Balazard, Félix Andreux, Mathieu Methodology Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings |
| topic | Methodology Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2311.16984 |