Cost-Effectiveness of Pazopanib in Advanced Soft-Tissue Sarcoma in Canada
Bibliographic record
Abstract
BACKGROUND: In the phase iii palette trial of pazopanib compared with placebo in patients with advanced or metastatic soft-tissue sarcoma (sts) who had received prior chemotherapy, pazopanib treatment was associated with improved progression-free survival (pfs). We used an economic model and data from palette and other sources to evaluate the cost-effectiveness of pazopanib in patients with advanced sts who had already received chemotherapy. METHODS: We developed a multistate model to estimate expected pfs, overall survival (os), lifetime sts treatment costs, and quality-adjusted life-years (qalys) for patients receiving pazopanib or placebo as second-line therapy for advanced sts. Cost-effectiveness was calculated alternatively from the health care system and societal perspectives for the province of Quebec. Estimated pfs, os, incidence of adverse events, and utilities values for pazopanib and placebo were derived from the palette trial. Costs were obtained from published sources. RESULTS: Compared with placebo, pazopanib is estimated to increase qalys by 0.128. The incremental cost of pazopanib compared with placebo is CA$20,840 from the health care system perspective and CA$15,821 from the societal perspective. The cost per qaly gained with pazopanib in that comparison is CA$163,336 from the health care system perspective and CA$124,001 from the societal perspective. CONCLUSIONS: Compared with placebo, pazopanib might be cost-effective from the Canadian health care system and societal perspectives depending on the threshold value used by reimbursement authorities to assess novel cancer therapies. Given the unmet need for effective treatments for advanced sts, pazopanib might nevertheless be an appropriate alternative to currently used treatments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".