Sunitinib, sorafenib, temsirolimus, or bevacizumab in the treatment of metastatic renal cell carcinoma (RCC): A review of health economic evaluations
Bibliographic record
Abstract
e17539 Background: Renal cell carcinoma (RCC) is the most prevalent kidney cancer and the 5-year overall survival figure in metastatic disease (mRCC) is about 10%. New targeted drugs (sunitinib, sorafenib, bevacizumab, temsirolimus) have shown activity in the treatment of mRCC, but they are all associated with a significant burden of cost. Methods: To support decision makers in their allocation of resources, cost-effectiveness models are constructed to compare the costs and outcomes of anticancer therapy. The PubMed, ASCO abstracts, Google, and the Igaku Chuo Zasshi databases were searched in November 2008 with key terms: kidney, renal, cancer, cost, sunitinib, sorafenib, temsirolimus, and bevacizumab. Seven studies reporting data on cost-effectiveness were revealed. Three of them were published in full text versions. The countries of application were United Kingdom, Canada, United States, Finland, and Japan. An analytical checklist was applied to the seven economic evaluations. Results: The review reveals figures of cost per LYG or QALY in the range €22,648 to €203,692, depending on line setting and drug focused. The results were limited by short follow up periods and the consequently fact that premature data had to be implemented in the Markov models. When compared, sunitinib has the lowest cost-effectiveness figure. Second-line therapy dose not seem to offer valid incremental cost-effectiveness ratios (ICERs) below accepted cost-effectiveness thresholds. As long as cross-over to the experimental arm is allowed (based on improvement in progression free survival) overall survival data are difficult to interpret and the cost difference between the treatment and the control arm minimised. Conclusions: The review revealed ICERs with a wide range. Sunitinib has the lowest cost-effectiveness figure. Second-line therapy does not look cost-effective. [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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".