A correlation analysis to assess event-free survival as a trial-level surrogate for overall survival in early breast cancer
Bibliographic record
Abstract
BACKGROUND: Event-free survival (EFS) has been listed on the FDA Table of Surrogate Endpoints as a surrogate measure that can be considered for accelerated or traditional approval in breast cancer. However, no studies have evaluated the correlation between the treatment effects on EFS and treatment effects on overall survival (OS). METHODS: We performed a systematic search of the literature until May 2020 according to the PRISMA guideline for all published randomized controlled trials (RCTs) in early breast cancer in the neoadjuvant setting. Data on EFS and OS, including the hazard ratio (HR) and 95% confidence intervals (CI), were extracted from each study and the association between the trial-level EFS HR and the trial-level OS HR was estimated using a linear mixed-effects model on the log scale. FINDINGS: = 0.58 [95% CI: -0.32-1.48]) and the coefficient of determination confirmed a moderate trial-level association between log HRs for OS and EFS (R² 0.76 [95% CI 0.34-1.00], but with wide confidence intervals. INTERPRETATION: Treatment effects in EFS are moderately correlated with treatment effects in OS in early breast cancer in the neoadjuvant setting, but the association was not significant. Thus, there is currently insufficient evidence to support EFS for use as a surrogate endpoint for traditional approval, although it may be considered for accelerated approval. FUNDING: Arnold Ventures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.228 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".