Disease-free survival as a surrogate for overall survival in early-stage pancreatic cancer trials: a correlation meta-analysis
Bibliographic record
Abstract
Objective: Disease-free survival (DFS) is frequently used as the primary endpoint in trials of adjuvant and neoadjuvant therapies for early-stage pancreatic cancer (PC), but its validity as a surrogate for overall survival (OS) remains uncertain. This study evaluates the strength and consistency of DFS as a surrogate for OS in early-stage PC trials. Methods and analysis: This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Trials of early-stage PC involving drug therapy were identified through PubMed, Embase and Cochrane CENTRAL databases, and data on DFS and OS with HR and CIs were extracted. Trial-level surrogacy of DFS for OS was assessed using weighted linear regression, calculating the coefficient of determination (R²) and surrogate threshold effect (STE). Results: 29 trials involving 6777 patients were included. In adjuvant trials, HR of DFS strongly correlated with HR of OS (R²=0.70) at trial-level, with the STE of 0.94 indicating the maximum DFS HR beyond which OS benefit was unlikely. The correlation strength differed between phase III (R²=0.71) versus phase II (R²=0.67) trials. This correlation was stronger in trials including radiation therapy (R²=0.81) and trials in the neoadjuvant setting (R²=0.90). No trial in our study was a registration trial of a new molecule and all involved chemotherapy. Conclusion: Treatment effects on DFS had a strong correlation with treatment effects on OS, making DFS a potential surrogate endpoint for OS in early-stage PC trials of cytotoxic chemotherapies, but its use in registration trials requires careful consideration due to variability across treatment settings and trial designs. PROSPERO registration number: CRD42024595441.
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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 | 0.113 | 0.207 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.055 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".