Nivolumab in the Treatment of Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
INTRODUCTION: Nivolumab improves overall survival and health-related quality of life compared with everolimus in metastatic renal cell carcinoma (mRCC). This study assesses the cost-utility of nivolumab from the Canadian health care payer perspective. MATERIALS AND METHODS: To evaluate the cost-utility of nivolumab, a Markov cohort model that incorporated data from the phase 3 CheckMate-025 trial and other sources was developed. The incremental cost per quality-adjusted life month (QALM) gained for nivolumab was calculated. A lifetime horizon was used in the base-case with costs and outcomes discounted 3% annually. The probabilities of progression and death from cancer and utility values were captured from the CheckMate-025 trial. Expected costs were based on Ontario fees and other sources. Scenario and sensitivity analyses were conducted to assess uncertainty. RESULTS: Compared with everolimus, nivolumab provided an additional 4.2 QALM at an incremental cost of $34,153. The resulting incremental cost-effectiveness ratio was $8138/QALM gained. Assuming a willingness to pay (WTP) threshold of $4167/QALM ($50,000/quality-adjusted life-year [QALY]), nivolumab was not cost-effective. In 1-way sensitivity analyses, nivolumab cost, median overall survival, and median treatment duration were sensitive to changes. Furthermore, the results were sensitive to the WTP threshold and nivolumab became a cost-effective strategy with a WTP of $8333/QALM ($100,000/QALY). CONCLUSIONS: Compared with everolimus, nivolumab is unlikely to be cost-effective for the treatment of mRCC from a Canadian health care perspective with its current price assuming a WTP of $50,000/QALY. Although mRCC patients derive a meaningful clinical benefit from nivolumab, considerations should be given to avoid drug wastage and increase the WTP threshold to render this strategy more affordable.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".