Effect of crossover from placebo to darolutamide on overall survival in men with non-metastatic prostate cancer: sensitivity analyses from the randomised phase 3 ARAMIS study
Bibliographic record
Abstract
BACKGROUND: In the phase 3 ARAMIS study (NCT02200614), darolutamide significantly improved metastasis-free survival in patients with non-metastatic castration-resistant prostate cancer (nmCRPC). Following the primary analysis, the study was unblinded, and placebo recipients were permitted to cross over to open-label darolutamide. Despite crossover, darolutamide significantly improved overall survival (OS). We conducted sensitivity analyses to estimate the effect of placebo-darolutamide crossover on OS. METHODS: Patients with nmCRPC were randomised to oral darolutamide 600 mg twice daily (n = 955) or placebo (n = 554). Prespecified (rank-preserving structural failure time [RPSFT] and iterative parameter estimation [IPE]) and post hoc (OS-adjusted censoring and inverse probability of censoring weighting [IPCW], with weightings for baseline testosterone and prostate-specific antigen) sensitivity analyses were conducted. RESULTS: After unblinding, 170 of 554 placebo recipients (30.7%) crossed over to darolutamide. At the final OS intention-to-treat analysis (median 11.2 months after unblinding), darolutamide significantly improved OS by 31% versus placebo (hazard ratio [HR] 0.69, 95% confidence interval [CI] 0.53-0.88; P = 0.003). The benefit increased in the analyses adjusting for crossover is as follows: RPSFT HR 0.68, 95% CI 0.51-0.90; P = 0.007; IPE HR 0.66, 95% CI 0.51-0.84; P < 0.001; OS-adjusted censoring HR 0.59, 95% CI 0.45-0.76; IPCW HR 0.63, 95% CI 0.48-0.81. The favourable safety profile of darolutamide was maintained, including in crossover patients. CONCLUSIONS: After adjusting for crossover, darolutamide reduced the risk of death by up to 41% in patients with nmCRPC. The effect of darolutamide on OS may have been underestimated in the original intention-to-treat analysis.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".