Association of race and survival in patients treated with apalutamide: Pooled analysis of two phase 3 trials
Bibliographic record
Abstract
BACKGROUND: Clinical studies have shown that outcomes of patients with prostate cancer could vary depending on race. In this study, the authors sought to determine if the treatment effect of apalutamide, an androgen receptor pathway inhibitor (ARPI), on overall survival (OS) varies depending on the race of the patient. METHODS: This pooled analysis includes individual patient data from two phase 3 trials, TITAN and SPARTAN, which randomized patients to androgen deprivation therapy (ADT) ± apalutamide in metastatic hormone-sensitive and nonmetastatic castration-resistant prostate cancer, respectively. Race was self-identified and categorized as Asian, Black, White, and Others categories. The authors applied a stratified (stratification for the trial) multivariable Cox proportional hazards regression model to determine heterogeneity of treatment effect on OS after adjustment for age, performance status, body mass index, T- and N-stage, Gleason score, comorbidities, and exposure to statins and metformin. RESULTS: Overall, 2190 patients were included: 16.9% patients were Asian, 3.7% were Black, 67.4% were White, and 12.0% were from the Others category. The authors did not find any significant heterogeneity of treatment effect from apalutamide on OS across racial groups (interaction-p = .46). Among ADT plus apalutamide-treated patients, there was no association of race with OS (hazard ratio for Asian, 0.77 [95% CI, 0.56-1.06]; Black, 0.82 [95% CI, 0.49-1.37]; and Others, 1.00 [95% CI, 0.75-1.34], all compared to White). CONCLUSIONS: In this study, the authors did not find any evidence of difference in the treatment effect of apalutamide on OS across patients of different races, although interpretation remains limited by poor representation of racial minorities. Among apalutamide-treated patients, there was no association of race 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 | 0.039 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.029 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".