Surrogacy analysis of prostate-specific antigen (PSA) decline for improved overall survival (OS) with enzalutamide (ENZ) in AFFIRM.
Bibliographic record
Abstract
266 Background: ENZ significantly increased OS for men with metastatic castration-resistant prostate cancer (mCRPC) vs. placebo and significantly decreased PSA levels. However, the prognostic significance and role of PSA falls as a surrogate for OS are not established. Methods: Men in the AFFIRM trial were grouped by maximal unconfirmed PSA decline during the 1st 90 days of treatment in a post-hoc analysis. Each PSA decline criterion was assessed for surrogacy for OS by the proportion of treatment effect (PTE)-explained and Prentice criteria. We also assessed the association of PSA decline with OS, progression-free survival (PFS), and pain response. Results: ENZ improved OS (hazard ratio 0.63, p < 0.001) and was associated with higher rates of PSA declines when compared to placebo (odds ratio > 19.0, p < 0.001). Greater declines in PSA were associated with longer OS, PSA PFS, radiographic PFS, and higher pain response when compared with no PSA decline or PSA increase (table). All decline measures from baseline were highly prognostic for OS and several ( > 0%, ≥ 30%, ≥ 50% declines) explained a proportion of the treatment effect (PTE 1.07–1.29, 95% CI lower bounds > 0.63), in which treatment was no longer significant after adjustment for the decline measures (p > 0.20). Full surrogacy was not demonstrated. Conclusions: In AFFIRM, > 0, ≥30%, and ≥50% PSA declines within 90 days of treatment fulfilled Prentice surrogacy criteria 1–3. Prentice 4, equivalency of survival adjusting for PSA decline outcomes, could not be demonstrated. PSA declines are associated with longer PFS and improved pain response. External prospective validation is needed. Clinical trial information: NCT00974311. [Table: see text]
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".