Efficacy and safety of treatments for metastatic castration-sensitive prostate cancer: A comprehensive network meta-analysis including final ARANOTE data
Bibliographic record
Abstract
BACKGROUND: Despite the proven efficacy of androgen deprivation therapy (ADT) combined with androgen receptor pathway inhibitors (ARPIs) in metastatic castration-sensitive prostate cancer (mCSPC), many patients still receive ADT monotherapy due to safety concerns. This reliance on ADT monotherapy underscores the need for education on the comparative effectiveness and safety of available therapies versus ADT. We evaluated the efficacy and safety of alternative treatment combinations, incorporating final data from the recent ARANOTE Phase III trial. METHODS: We conducted network meta-analysis (NMA) to evaluate progression-free survival (PFS) and overall survival (OS), incorporating heterogeneity assessment through subgroup analyses. Additionally, we performed a separate class effect NMA. We analysed grade 3-5 adverse events (AEs), serious AEs, and discontinuation due to AEs. We estimated hazard ratios (HRs) for efficacy, rate ratios (RRs) for safety, 95% credible intervals (CrI), and the surface under the cumulative ranking area (SUCRA) to rank treatments by efficacy and safety. RESULTS: Darolutamide (DAR) + docetaxel (DOC) + ADT showed the highest effect size [HR of 0.27 (95% CrI: 0.18, 0.39)] and the highest ranking (SUCRA: 0.97) across the base case and several subgroups on the PFS outcome. On OS, DAR + DOC + ADT similarly achieved the lowest HR of 0.52 (0.43, 0.64) and the highest ranking (SUCRA of 0.95). Safety analyses showed that grade 3-5 AEs were more frequent with docetaxel combinations, with ABI + DOC + ADT having the highest risk of grade 3-5 AEs. DAR + ADT was ranked best on all safety outcomes, outperforming other doublets and comparable to ADT monotherapy. CONCLUSIONS: This NMA supports the superior efficacy of ARPI combinations against ADT monotherapy, for both OS and PFS. While DAR + ADT demonstrated comparable efficacy to other doublet combinations, it offered a superior safety profile, making it an effective and safe option for managing patients with mCSPC.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.054 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".