Cost-Effectiveness of Pazopanib Compared with Sunitinib in Metastatic Renal Cell Carcinoma in Canada
Bibliographic record
Abstract
BACKGROUND: In Canada and elsewhere, pazopanib and sunitinib-tyrosine kinase inhibitors targeting the vascular endothelial growth factor receptors-are recommended as first-line treatment for patients with metastatic renal cell carcinoma (mrcc). A large randomized noninferiority trial of pazopanib versus sunitinib (comparz) demonstrated that the two drugs have similar efficacy; however, patients randomized to pazopanib experienced better health-related quality of life (hrqol) and nominally lower rates of non-study medical resource utilization. METHODS: The cost-effectiveness of pazopanib compared with sunitinib for first-line treatment of mrcc from a Canadian health care system perspective was evaluated using a partitioned-survival model that incorporated data from comparz and other secondary sources. The time horizon of 5 years was based on the maximum duration of follow-up in the final analysis of overall survival from the comparz trial. Analyses were conducted first using list prices for pazopanib and sunitinib and then by assuming that the prices of sunitinib and pazopanib would be equivalent. RESULTS: Based on list prices, expected costs were CA$10,293 less with pazopanib than with sunitinib. Pazopanib was estimated to yield 0.059 more quality-adjusted life-years (qalys). Pazopanib was therefore dominant (more qalys and lower costs) compared with sunitinib in the base case. In probabilistic sensitivity analyses, pazopanib was dominant in 79% of simulations and was cost-effective in 90%-100% of simulations at a threshold cost-effectiveness ratio of CA$100,000. Assuming equivalent pricing, pazopanib yielded CA$917 in savings in the base case, was dominant in 36% of probabilistic sensitivity analysis simulations, and was cost-effective in 89% of simulations at a threshold cost-effectiveness ratio of CA$100,000. CONCLUSIONS: Compared with sunitinib, pazopanib is likely to be a cost-effective option for first-line treatment of mrcc from a Canadian health care perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".