Cost-Effectiveness of Pazopanib in Advanced Soft Tissue Sarcoma in the United Kingdom
Bibliographic record
Abstract
In the phase III PALETTE trial, pazopanib improved progression-free survival (PFS) compared with placebo in patients with advanced/metastatic soft tissue sarcomas (mSTS) who had received prior chemotherapy. We used a multistate model to estimate expected PFS, overall survival (OS), lifetime STS treatment costs, and quality-adjusted life-years (QALYs) for patients receiving pazopanib, placebo, trabectedin, ifosfamide, or gemcitabine plus docetaxel as second-line mSTS therapies. The cost-effectiveness of pazopanib was expressed as the incremental costs per QALY gained. Estimates of PFS/OS, adverse events, and utilities for pazopanib and placebo were from the PALETTE trial. Estimates of relative effectiveness of the other comparators were from an unadjusted indirect comparison versus pazopanib. Costs were from published sources. Pazopanib is estimated to increase QALYs by 0.128 and costs by £7,976 versus placebo; cost per QALY gained with pazopanib versus placebo is estimated to be £62,000. Compared with the other chemotherapies, pazopanib provides similar QALYs at a lower cost. Pazopanib may not be cost-effective versus placebo but may be cost-effective versus the most commonly used active treatments, although this conclusion is uncertain. Given the unmet need for effective treatments for mSTS, pazopanib may be an appropriate alternative to some currently used medications in the United Kingdom.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".