Prostate-Specific Antigen and Pain Surrogacy Analysis in Metastatic Hormone-Refractory Prostate Cancer
Bibliographic record
Abstract
PURPOSE: It is currently unclear if early prostate-specific antigen (PSA) or pain improvements are adequate surrogates for overall survival in men with metastatic hormone-refractory prostate cancer (HRPC). Here we examined various degrees of PSA decline and pain response as surrogates for the survival benefit observed in the TAX327 trial. PATIENTS AND METHODS: In the TAX327 trial, 1,006 men with HRPC were randomly assigned to receive docetaxel in two schedules, or mitoxantrone, each with prednisone: 989 men provided data on 3-month PSA decline. Surrogacy was examined for post-treatment changes in PSA and pain response using Cox proportional hazards models to calculate the proportion of treatment effect (PTE) explained by each potential surrogate. RESULTS: A > or = 30% PSA decline within 3 months of treatment initiation provides the highest degree of surrogacy, with a PTE of 0.66 (95% CI, 0.23 to 1.0), and was associated with a hazard ratio (HR) of 0.50 (95% CI, 0.43 to 0.58) for overall survival after adjusting for treatment effect. Introduction of a > or = 30% PSA decline is predictive of survival regardless of treatment arm. Other changes in PSA or PSA kinetics, PSA normalization, and pain responses were highly prognostic but weaker surrogates for survival. CONCLUSION: In the TAX327 trial, a PSA decline of > or = 30% within 3 months of chemotherapy initiation had the highest degree of surrogacy for overall survival, confirming data from the Southwest Oncology Group 9916 trial. However, given the wide CIs around the estimate of this moderate surrogate effect, overall survival should remain the preferred end point for phase III trials of cytotoxic agents in HRPC.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".