Economic Evaluation of Bevacizumab for Treatment of Platinum-Resistant Recurrent Ovarian Cancer in Canada
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer is a leading cause of cancer-related mortality. Although the disease is relatively rare, it carries a disproportionately large morbidity burden. OBJECTIVE: We conducted a cost-utility analysis from a Canadian public payer perspective to determine the cost effectiveness of bevacizumab, a newly available treatment option for recurrent ovarian cancer. METHODS: Using a 7-year time horizon, a three health-state cohort-based partitioned survival model was developed to assess the cost utility of bevacizumab plus chemotherapy (BEV) versus chemotherapy alone. We reconstructed individual patient data from published Kaplan-Meier curves. Clinical parameters, including progression-free survival and overall survival, were derived from the AURELIA phase III randomized controlled trial. Costs, resource utilization and utility values from recent Canadian sources were used to populate the model. Results were presented using incremental cost-utility ratios (ICURs). Uncertainty was examined through univariate and probabilistic sensitivity analyses. RESULTS: The reconstructed individual patient data matched the AURELIA trial results. Total costs for the BEV and chemotherapy treatment arms were $Can79,086 and $Can54,982, respectively. Total estimated quality-adjusted life-years (QALYs) were 1.1055 and 0.9926 for the BEV and chemotherapy arms, respectively. The ICUR was estimated to be $Can213,424 per QALY gained. At a willingness-to-pay threshold of $Can100,000 per QALY gained, the probability of BEV being cost effective was 0. CONCLUSIONS: The results of our analysis suggest that the addition of bevacizumab to single-agent chemotherapy treatment, while improving patient outcomes, is unlikely to be cost effective in this Canadian patient population. The results also provide some preliminary validation for use of individual patient data-reconstruction techniques in pharmacoeconomic evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".