Surrogate End Points for Median Overall Survival in Metastatic Colorectal Cancer: Literature-Based Analysis From 39 Randomized Controlled Trials of First-Line Chemotherapy
Bibliographic record
Abstract
PURPOSE: Our aims were to determine the correlations between progression-free survival (PFS), time to progression (TTP), and response rate (RR) with overall survival (OS) in the first-line treatment of metastatic colorectal cancer (MCRC), and to identify a potential surrogate for OS. METHODS: Randomized trials of first-line chemotherapy in MCRC were identified, and statistical analyses were undertaken to evaluate the correlations between the end points. RESULTS: Thirty-nine randomized controlled trials were identified containing a total of 87 treatment arms. Among trials, the nonparametric Spearman rank correlation coefficient (r(s)) between differences (Delta) in surrogate end points (DeltaPFS, DeltaTTP, and DeltaRR) and DeltaOS were 0.74 (95% CI, 0.47 to 0.88), 0.52 (95% CI, 0.004 to 0.81), 0.39 (95% CI, 0.08 to 0.63), respectively. The r(s) for DeltaPFS was not significantly different from the r(s) DeltaTTP (P = .28). Linear regression analysis was performed using hazard ratios for PFS and OS. There was a strong relationship between hazard ratios for PFS and OS; the slope of the regression line was 0.54 +/- 0.10, indicating that a novel therapy producing a 10% risk reduction for PFS will yield an estimated 5.4% +/- 1% risk reduction for OS. CONCLUSION: In first-line chemotherapy trials for MCRC, improvements in PFS are strongly associated with improvements in OS. In this patient population, PFS may be an appropriate surrogate for OS. As a clinical end point, PFS offers increased statistical power at a given time of analysis and a significant lead time advantage compared with OS.
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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 | 0.064 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".