Comparative efficacy and safety of adjuvant nivolumab versus other treatments in adults with resected melanoma: a systematic literature review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors and targeted therapies are approved for adjuvant treatment of patients with resected melanoma; however, they have not been compared in randomized controlled trials (RCTs). We compared the efficacy and safety of adjuvant nivolumab with other approved treatments using available evidence from RCTs in a Bayesian network meta-analysis (NMA). METHODS: A systematic literature review was conducted through May 2019 to identify relevant RCTs evaluating approved adjuvant treatments. Outcomes of interest included recurrence-free survival (RFS)/disease-free survival (DFS), distant metastasis-free survival (DMFS), all-cause grade 3/4 adverse events (AEs), discontinuations, and discontinuations due to AEs. Time-to-event outcomes (RFS/DFS and DMFS) were analyzed both assuming that hazard ratios (HRs) are constant over time and that they vary. RESULTS: Of 26 identified RCTs, 19 were included in the NMA following a feasibility assessment. Based on HRs for RFS/DFS, the risk of recurrence with nivolumab was similar to that of pembrolizumab and lower than that of ipilimumab 3 mg/kg, ipilimumab 10 mg/kg, or interferon. Risk of recurrence with nivolumab was similar to that of dabrafenib plus trametinib at 12 months, however, was lower beyond 12 months (HR [95% credible interval] at 24 months, 0.46 [0.27-0.78]; at 36 months, 0.28 [0.14-0.59]). Based on HRs for DMFS, the risk of developing distant metastases was lower with nivolumab than with ipilimumab 10 mg/kg or interferon and was similar to dabrafenib plus trametinib. CONCLUSION: Adjuvant therapy with nivolumab provides an effective treatment option with a promising risk-benefit profile.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".