The Cost-Effectiveness of Bevacizumab for the Treatment of Advanced Ovarian Cancer in Canada
Bibliographic record
Abstract
BACKGROUND: The overall survival (os) analysis of the icon7 trial demonstrated that frontline ovarian cancer patients with a high risk of progression (stage iii suboptimally debulked, and stage iii or iv with unresectable disease) benefited from the addition of bevacizumab to standard chemotherapy compared with standard chemotherapy alone. The objective of the present study was to investigate the cost-effectiveness, from a Canadian publicly funded perspective, of adding bevacizumab to frontline treatment of ovarian cancer at high risk of progression. METHODS: An area-under-the-curve, Markov-structured model was used to estimate the cost-effectiveness of the treatments. Long-term progression-free survival (pfs) and os were extracted from the icon7 trial (subgroup at high risk of relapse) and extrapolated by parametric time-to-event functions over a time horizon of 10 years. Canadian pfs health state utility values were obtained from the EQ-5D (EuroQoL Group, Rotterdam, Netherlands) questionnaires in the icon7 high-risk patient population. Canadian post-progression utility values were consistent with those for other gynecologic cancers. Cost inputs were informed by public sources. An annual 5% efficacy and cost discount rate was applied. A probabilistic sensitivity analysis and one-way sensitivity analyses were conducted. RESULTS: Ovarian cancer patients at high risk of progression receiving bevacizumab plus standard chemotherapy experienced a mean incremental quality-adjusted life year (qaly) gain of 0.374 years. At an additional cost of $35,901.54, the incremental cost-effectiveness ratio (icer) for the addition of bevacizumab to standard chemotherapy, relative to standard chemotherapy alone, was $95,942 per qaly. CONCLUSIONS: No formal health technology assessment willingness-to-pay threshold exists in Canada. However, at a threshold of $100,000 per qaly, bevacizumab in addition to chemotherapy is a cost-effective alternative for ovarian cancer patients who are at high risk of progression (stage iii suboptimally debulked, and stage iii or iv with unresectable disease). Using the $100,000 per qaly threshold in a probabilistic sensitivity analysis, it was determined that, compared with standard chemotherapy, the addition of bevacizumab to chemotherapy is cost-effective in 56% of tested scenarios.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".