Novel framework for the validation of intermediate clinical endpoints in oncology real-world data.

Authors

Chris Labaki, Ziad Bakouny, Thibaut Sanglier, Eddy Saad, Miguel Valero Martin, Paolo Tarantino, Andrew L Schmidt, Jinjoo Shim, David A Braun, Fanny Bouquet, Maureen Joyce, Marc Machaalani, Eliezer M Van Allen, Wanling Xie, Sara M Tolaney, Toni K Choueiri

Published in

Journal of the National Cancer Institute. Oct 19, 2026. Epub Oct 19, 2026.

Abstract

The role of real-world data in oncology has been substantially increasing, helping to answer clinically unmet needs. No valid framework for real-world data is currently available for the evaluation of surrogate endpoints, with no intermediate clinical endpoint (ICE) being validated as a surrogate for overall survival (OS) across most cancer types.
A real-world retrospective cohort study was conducted using the nationwide US Flatiron Health database. Four ICEs were evaluated real-world progression-free survival (rwPFS), and three exposure-derived endpoints: Time to Next line of Therapy (TTNT), Time to Last Treatment Administration (TTLA) of first line therapy, and Time to End of First-Line Therapy (TTEFL). Two conditions were evaluated to establish surrogacy: correlation between ICE and OS (condition 1), and correlation of group effects (condition 2).
A total of 19,164 patients with metastatic breast cancer (MBC) were included. Median OS was 36.3 (95%CI, 35.6-37.2) months across the study population. For condition 1, a high correlation was found between each of rwPFS, TTNT and TTLA, and OS (Kendall's tau [τ] ≥ 0.5; R2 ≥ 0.7 between 36-month OS and 12-month rates for each ICE across subgroups of interest). For condition 2, group effects were found to be strongly correlated only between rwPFS and OS (R2 ≥ 0.7 across each subgroup comparison).
Among patients with MBC, rwPFS is a strong surrogate for OS. This study establishes a novel framework applicable to real-world data for the validation of intermediate clinical endpoints. This study can serve as a blueprint for future efforts of real-world data analysis.

PMID:
42898006
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.

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