Cost-effectiveness analysis of cabozantinib compared with everolimus, axitinib, and nivolumab in subsequent line advanced renal cell carcinoma in Japan
Bibliographic record
Abstract
AIMS: The treatment landscape of renal cell carcinoma has changed with the introduction of targeted therapies. While the clinical benefit of cabozantinib is well-established for Japanese patients who have received prior treatment, the economic benefit remains unclear. The objective of this study was to assess the cost-effectiveness of cabozantinib compared with everolimus, axitinib, and nivolumab in patients with advanced renal cell carcinoma who have failed at least one prior therapy in Japan. METHODS: A cost-effectiveness model was developed using a partitioned survival approach and a public healthcare payer's perspective. Over a lifetime horizon, clinical and economic implications were estimated according to a three-health-state structure: progression-free, post-progression, and death. Key clinical inputs and utilities were derived from the METEOR trial, and a de novo network meta-analysis and cost data were obtained from publicly available Japanese data sources. Costs, quality-adjusted life-years, and incremental cost-effectiveness ratios were estimated. Costs and health benefits were discounted annually at 2%. RESULTS: Cabozantinib was more costly and effective compared with everolimus and axitinib, with deterministic incremental cost-effectiveness ratios of ¥5,375,559 and ¥2,223,138, respectively. Compared to nivolumab, cabozantinib was predicted to be less costly and more effective. Sensitivity and scenario analyses demonstrated that the key drivers of cost-effectiveness results were the estimation of overall survival and treatment duration, relative efficacy, drug costs, and subsequent treatment costs. LIMITATIONS: METEOR was an international trial but did not enroll any patients from Japan. Efficacy and safety data from METEOR were used as a proxy for the Japanese population following validation by clinical experts, and alternative assumptions specific to clinical practice in Japan were evaluated in scenario analyses. CONCLUSIONS: In Japan, cabozantinib is a cost-effective alternative to everolimus, axitinib, and nivolumab for the treatment of patients with advanced renal cell carcinoma who have received at least one prior line of therapy.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".