A Federated Data Analysis Approach for the Evaluation of Surrogate Endpoints
(2025) In Pharmaceutical Statistics 24(2).- Abstract
In clinical trials, surrogate endpoints, that are more cost-effective, occur earlier, or are more frequently measured, are sometimes used to replace costly, late, or rare true endpoints. Regulatory authorities typically require thorough evaluation and validation to accept these surrogate endpoints as reliable substitutes. To this end, the meta-analytic framework is considered a very viable approach to validate surrogates at both trial and individual levels. However, this framework requires data from multiple trials or centers, posing challenges when data sharing is not feasible. In this article, we propose a federated data analysis approach that allows organizations to maintain control over their datasets while still enabling surrogate... (More)
In clinical trials, surrogate endpoints, that are more cost-effective, occur earlier, or are more frequently measured, are sometimes used to replace costly, late, or rare true endpoints. Regulatory authorities typically require thorough evaluation and validation to accept these surrogate endpoints as reliable substitutes. To this end, the meta-analytic framework is considered a very viable approach to validate surrogates at both trial and individual levels. However, this framework requires data from multiple trials or centers, posing challenges when data sharing is not feasible. In this article, we propose a federated data analysis approach that allows organizations to maintain control over their datasets while still enabling surrogate validation through meta-analytic techniques. In this approach, there is no longer a need for raw data sharing. Instead, independent analyses are conducted at each organization. Thereafter, the results of these independent analyses are aggregated at a central analysis hub and the metrics for surrogate evaluation are extracted. We apply this approach to simulated and real clinical data, demonstrating how this federated approach can overcome data-sharing constraints and validate surrogate endpoints in decentralized settings.
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- author
- De Witte, Dries ; Abad, Ariel Alonso ; Stephenson, Diane ; Karten, Yashmin ; Leuzy, Antoine LU ; Klein, Gregory and Molenberghs, Geert
- organization
- publishing date
- 2025-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- federated data, meta-analytic framework, surrogacy
- in
- Pharmaceutical Statistics
- volume
- 24
- issue
- 2
- article number
- e70003
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:85219265150
- pmid:40000152
- ISSN
- 1539-1604
- DOI
- 10.1002/pst.70003
- language
- English
- LU publication?
- yes
- id
- 29bb9eda-d250-41c3-87ac-7dd439853f7d
- date added to LUP
- 2025-06-24 09:47:30
- date last changed
- 2025-06-24 09:48:10
@article{29bb9eda-d250-41c3-87ac-7dd439853f7d,
abstract = {{<p>In clinical trials, surrogate endpoints, that are more cost-effective, occur earlier, or are more frequently measured, are sometimes used to replace costly, late, or rare true endpoints. Regulatory authorities typically require thorough evaluation and validation to accept these surrogate endpoints as reliable substitutes. To this end, the meta-analytic framework is considered a very viable approach to validate surrogates at both trial and individual levels. However, this framework requires data from multiple trials or centers, posing challenges when data sharing is not feasible. In this article, we propose a federated data analysis approach that allows organizations to maintain control over their datasets while still enabling surrogate validation through meta-analytic techniques. In this approach, there is no longer a need for raw data sharing. Instead, independent analyses are conducted at each organization. Thereafter, the results of these independent analyses are aggregated at a central analysis hub and the metrics for surrogate evaluation are extracted. We apply this approach to simulated and real clinical data, demonstrating how this federated approach can overcome data-sharing constraints and validate surrogate endpoints in decentralized settings.</p>}},
author = {{De Witte, Dries and Abad, Ariel Alonso and Stephenson, Diane and Karten, Yashmin and Leuzy, Antoine and Klein, Gregory and Molenberghs, Geert}},
issn = {{1539-1604}},
keywords = {{federated data; meta-analytic framework; surrogacy}},
language = {{eng}},
number = {{2}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Pharmaceutical Statistics}},
title = {{A Federated Data Analysis Approach for the Evaluation of Surrogate Endpoints}},
url = {{http://dx.doi.org/10.1002/pst.70003}},
doi = {{10.1002/pst.70003}},
volume = {{24}},
year = {{2025}},
}