Network Meta-analysis of Progression-Free Survival and Overall Survival in First-Line Treatment of BRAF Mutation-Positive Metastatic Melanoma
Bibliographic record
Abstract
INTRODUCTION: The present study aimed to inform an economic evaluation of dabrafenib and trametinib combination as first-line treatment of metastatic melanoma in a Canadian setting. A network meta-analysis was conducted to estimate hazard ratios (HRs) for progression-free survival (PFS)and overall survival (OS) of dabrafenib plus trametinib versus other first-line treatments of BRAF mutation-positive metastatic melanoma including dabrafenib, trametinib, vemurafenib, ipilimumab, and dacarbazine (DTIC). METHODS: HRs for PFS and OS were from randomized controlled trials identified from systematic literature reviews. HRs for PFS and OS (adjusted for crossover as appropriate) were analyzed using multivariate and univariate Bayesian network meta-analysis. RESULTS: In multivariate network-meta analyses (HRs for PFS and OS estimated simultaneously to account for the correlation of treatment effects on PFS and OS), HRs (95% credible interval) for PFS and OS favored dabrafenib plus trametinib [PFS: 0.23 (0.18-0.29) versus DTIC, 0.32 (0.24-0.42) versus ipilimumab plus DTIC, 0.52 (0.32-0.83) versus trametinib, 0.57 (0.48-0.69) versus vemurafenib, and 0.59 (0.50-0.71) versus dabrafenib]; OS [0.41 (0.29-0.56) versus DTIC, 0.52 (0.38-0.71) versus ipilimumab plus DTIC, 0.68 (0.47-0.95) versus trametinib, 0.69 (0.57-0.84) versus vemurafenib, and 0.72 (0.60-0.85) versus dabrafenib]. The beneficial effects on OS of dabrafenib plus trametinib versus ipilimumab plus DTIC and versus trametinib were attenuated when HRs were estimated using univariate network meta-analysis (HRs for PFS and OS estimated separately). CONCLUSION: This analysis demonstrates improved PFS and OS with dabrafenib + trametinib versus dabrafenib, trametinib, vemurafenib, ipilimumab plus DTIC, and DTIC as first-line treatment for patients with BRAF mutation-positive metastatic melanoma. FUNDING: Novartis Pharmaceuticals.
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.034 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".