Event-Free Survival, a Prostate-Specific Antigen–Based Composite End Point, Is Not a Surrogate for Overall Survival in Men With Localized Prostate Cancer Treated With Radiation
Bibliographic record
Abstract
PURPOSE Recently, we have shown that metastasis-free survival is a strong surrogate for overall survival (OS) in men with intermediate- and high-risk localized prostate cancer and can accelerate the evaluation of new (neo)adjuvant therapies. Event-free survival (EFS), an earlier prostate-specific antigen (PSA)–based composite end point, may further expedite trial completion. METHODS EFS was defined as the time from random assignment to the date of first evidence of disease recurrence, including biochemical failure, local or regional recurrence, distant metastasis, or death from any cause, or was censored at the date of last PSA assessment. Individual patient data from trials within the Intermediate Clinical Endpoints in Cancer of the Prostate–ICECaP–database with evaluable PSA and disease follow-up data were analyzed. We evaluated the surrogacy of EFS for OS using a 2-stage meta-analytic validation model by determining the correlation of EFS with OS (patient level) and the correlation of treatment effects (hazard ratios [HRs]) on both EFS and OS (trial level). A clinically relevant surrogacy was defined a priori as an R 2 ≥ 0.7. RESULTS Data for 10,350 patients were analyzed from 15 radiation therapy–based trials enrolled from 1987 to 2011 with a median follow-up of 10 years. At the patient level, the correlation of EFS with OS was 0.43 (95% CI, 0.42 to 0.44) as measured by Kendall’s tau from a copula model. At the trial level, the R 2 was 0.35 (95% CI, 0.01 to 0.60) from the weighted linear regression of log(HR)-OS on log(HR)-EFS. CONCLUSION EFS is a weak surrogate for OS and is not suitable for use as an intermediate clinical end point to substitute for OS to accelerate phase III (neo)adjuvant trials of prostate cancer therapies for primary radiation therapy–based 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 | 0.061 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".