Economic Evaluation of Bevacizumab for the First-Line Treatment of Newly Diagnosed Glioblastoma Multiforme
Bibliographic record
Abstract
PURPOSE: The Avastin in Glioblastoma trial has shown that patients newly diagnosed with glioblastoma multiforme (GBM) treated with bevacizumab plus radiotherapy and temozolomide versus radiotherapy and temozolomide alone showed improvement in progression-free survival, possibly leading to a new indication for first-line use of bevacizumab in GBM. The cost-utility of this new intervention remains unknown; therefore, we developed a Markov model estimating the incremental cost-utility ratio (ICUR) from a Canadian public payer perspective. METHODS: We incorporated trial data for state transitions and treatment effects from the Avastin in Glioblastoma trial, costs and resource use data from Canadian published studies and databases, and utility parameters from published literature. We addressed uncertainty through one-way deterministic and probabilistic sensitivity analyses, extended the model to lifetime horizon and by another arm to compare first-line versus second-line use of bevacizumab on progression, performed value of information analysis, and performed US costing sensitivity analysis. RESULTS: Adding bevacizumab to radiotherapy and temozolomide resulted in increases of 0.13 quality-adjusted life-years (QALYs) and $80,000 per patient over 2-year time horizon at the base case analysis. The ICUR was $607,966/QALY (95% CI, $305,000/QALY to $2,550,000/QALY), with 0% chance of being cost effective at the $100,000/QALY willingness-to-pay threshold and never going below $450,000/QALY in the one-way sensitivity analysis. The ICUR using the US costing data was $787,519/QALY. The lifetime ICUR was $439,764/QALY (95% CI, $235,000/QALY to $1,520,000/QALY), never going below $350,000/QALY in the sensitivity analysis. Second-line use of bevacizumab on progression is more effective and less expensive than its first-line use. Value of information analysis revealed that future research is unwarranted. CONCLUSION: Bevacizumab has only limited effectiveness and is therefore not likely to be cost effective in treating adult patients with newly diagnosed GBM.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".