Challenges and opportunities of predicting overall survival benefit from improvements to recurrence-free survival in stage II/III melanoma: a correlation meta-analysis
Bibliographic record
Abstract
Background We evaluated the association between treatment effects on recurrence-free survival (RFS) and overall survival (OS) in randomized controlled trials (RCTs) studying resected stage II/III melanoma. Methods Hazard ratios (HRs) of RFS and OS were obtained from a literature review. Bivariate random-effects meta-analysis (BRMA) and weighted linear regression (WLR) models estimated correlations [95% confidence interval (CI)] between HR RFS and HR OS . Slopes and intercepts of surrogacy equations were estimated. Surrogate threshold effect was derived from WLR for various sample sizes. Validity and predictive performance of WLR were assessed using leave-one-out cross-validation. Sensitivity analyses evaluated impact of RCTs violating proportional hazards assumption, publication year, treatments' mechanism of action, and cancer stage. Results Across 30 RCTs, treatments included interferon-α ( n = 17), other immunotherapy-containing regimens ( n = 10), immune checkpoint inhibitors ( n = 3), and targeted therapies ( n = 2). BRMA (0.68, 95% CI 0.45-0.82) and WLR (0.71, 95% CI 0.42-0.87) estimated moderate correlation between HR RFS and HR OS . Surrogate threshold effect was 0.66/0.68 for studies with 800/1000 patients. Slope coefficients were statistically significant in both models (95% CI 0.09-0.61 BRMA; 95% CI 0.41-0.92 WLR). The 95% prediction intervals around the HR OS predicted by WLR accurately contained 29/31 (93.5%) of observed HR OS . Across sensitivity analyses correlations ranged between 0.69 and 0.84 (BRMA) and 0.55 and 0.77 (WLR). Conclusions Statistically meaningful correlation between HR RFS and HR OS can assist earlier predictions of OS benefit from improvements in RFS for RCTs in resected stage II/III melanoma and provide insights for the earlier evaluation of emerging therapies. Primary model predictions should be approached with caution as nearly half of the evidence base comprised interferon-α trials.
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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 | 0.131 | 0.228 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".