Cost-Effectiveness of Pembrolizumab Versus Carboplatin and Paclitaxel in Patients With Unresectable or Metastatic Melanoma After First-Line Treatment in China
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the cost-effectiveness of pembrolizumab compared with standard-of-care chemotherapy (paclitaxel + carboplatin [PC]) in patients with unresectable or metastatic melanoma after first-line treatment from a Chinese healthcare system perspective. METHODS: We conducted a partitioned-survival model with a 1-week cycle length and a 20-year base-case time horizon. Piecewise parametric models were fitted to KEYNOTE-006 trial data to estimate progression-free survival and overall survival for pembrolizumab, and a network meta-analysis was used to estimate the clinical outcomes for standard of care. Quality-adjusted life-years (QALYs) were calculated using EQ-5D data from KEYNOTE-006, applying Chinese-specific utility tariffs. Costs included drug acquisition, administration, adverse events, and disease management, reflecting the Chinese pricing system. Chinese-specific disease management costs were estimated based on clinical opinion on health state resource use and chemotherapy-related adverse events. Costs and outcomes were discounted at 5% annually. Multiple deterministic and probabilistic sensitivity analyses were performed to test the robustness of the results. RESULTS: In the base-case analysis, the treatment of pembrolizumab is estimated to yield 2.63 life-years (LYs) and 2.24 QALYs at an incremental cost of ¥372 316.46 versus PC. The incremental costs per LY and per QALY were ¥141 771.00 and ¥165 865.69, respectively, the latter being below a threshold of 3 times the per capita gross domestic product (￥193 932) in China, deemed as cost-effective according to the World Health Organization threshold. These findings were robust against a wide range of sensitivity analyses. CONCLUSIONS: Pembrolizumab is projected as cost-effective compared with PC in patients with unresectable or metastatic melanoma after first-line treatment in China.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".