Cost effectiveness of sorafenib versus best supportive care in advanced renal cell carcinoma in Canada
Bibliographic record
Abstract
5111 Background: Sorafenib is an oral multi-kinase inhibitor that targets tumour cell proliferation and tumour angiogenesis. In the TARGETs study (phase III trial), sorafenib plus best supportive care (BSC) significantly prolonged progression-free survival (PFS) compared with BSC alone (P<0.000001) in patients with advanced renal cell carcinoma (RCC). The objective of this study was to evaluate the costeffectiveness of sorafenib plus BSC versus BSC alone in advanced RCC from a Canadian provincial Ministry of Health perspective. Methods: A Markov model was developed to project the lifetime survival and costs associated with the two treatment groups. The model tracked patients with advanced RCC through three disease states - PFS, progression, and death. Resource utilization included drug, drug administration, physician visits, monitoring, and adverse events. Costs and survival benefits were discounted annually at 5%. Results: The lifetime per patient costs were $62,426 CDN and $18,898 CDN for sorafenib + BSC and BSC alone, respectively. The life-years gained (LYG) were higher for sorafenib relative to BSC. The incremental cost-effectiveness ratio (ICER) of sorafenib plus BSC versus BSC alone over a lifetime horizon was $36,046/LYG CDN (with a half cycle correction). Univariate sensitivity analyses yielded ICERs below $70,000/LYG CDN. Probabilistic sensitivity analyses showed that the results were moderately sensitive to the clinical variables and less sensitive to the cost variables, yielding ICERs below $100,000/LYG CDN in most cases. Conclusion: Sorafenib is cost effective with an ICER of $36,046/LYG CDN which is below the suggested cost effectiveness threshold of $100,000/QALY ($CDN 1992) or $130,860/QALY ($CDN 2006). [Table: see text]
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".