Nivolumab in the treatment of metastatic renal cell carcinoma: A cost-utility analysis.
Bibliographic record
Abstract
e18331 Background: Nivolumab was recently shown to improve overall survival (OS) and health-related quality of life compared to Everolimus in metastatic renal cell carcinoma (mRCC) patients previously treated with antiangiogenic therapies (CheckMate-025 trial). The aim of this study is to assess the cost-utility of Nivolumab versus Everolimus from the perspective of the Canadian publicly funded healthcare system. Methods: To evaluate the cost-utility of Nivolumab versus Everolimus, a Markov cohort model that incorporated data from the phase 3 CheckMate-025 trial and other sources was developed. The outcomes of interest were healthcare costs, life-months and quality-adjusted life-months (QALMs) gained with Nivolumab as well as the incremental cost-effectiveness ratio (ICER), and the incremental net monetary benefit. A lifetime time horizon was used in the base case with costs and outcomes discounted 5% 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 (SAs) were conducted to assess uncertainty. Results: Compared to Everolimus, treatment with Nivolumab provided an additional 3.9 QALMs at an incremental cost of 33,386 Canadian dollars (CAD). The resulting ICER was 8,608CAD per QALM gained. With a willingness-to-pay (WTP) of 50,000CAD per Quality-adjusted life-year (QALY) ( = 4,167CAD per QALM), Nivolumab was not cost-effective in the base case. In one-way SAs, Nivolumab cost, median OS and treatment duration on Nivolumab were sensitive to changes with plausible threshold values. Assuming a WTP of 100,000CAD per QALY ( = 8,334CAD per QALM) and a scenario of Nivolumab cost with no drug wastage, Nivolumab became a cost-effective strategy with an ICER of 7,881CAD per QALM. Conclusions: With its current price , Nivolumab is unlikely to be cost-effective compared with Everolimus for previously treated mRCC patients from a Canadian healthcare payer perspective. While mRCC patients derive a meaningful clinical benefit from Nivolumab, considerations should be given to reduce 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".