Bevacizumab for Metastatic Colorectal Cancer: A Global Cost-Effectiveness Analysis
Bibliographic record
Abstract
BACKGROUND: In the U.S., the addition of bevacizumab to first-line chemotherapy in metastatic colorectal cancer (mCRC) has been demonstrated to provide 0.10 quality-adjusted life years (QALYs) at an incremental cost-effectiveness ratio (ICER) of $571,000/QALY. Due to variability in pricing, value for money may be different in other countries. Our objective was to establish the cost-effectiveness of bevacizumab in mCRC in the U.S., U.K., Canada, Australia, and Israel. METHODS: We performed the analysis using a previously established Markov model for mCRC. Input data for efficacy, adverse events, and quality of life were considered to be generalizable and therefore identical for all countries. We used country-specific prices for medications, administration, and other health service costs. All costs were converted from local currency to U.S. dollars at the exchange rates in March 2016. We conducted one-way and probabilistic sensitivity analyses (PSA) to assess the model robustness across parameter uncertainties. RESULTS: Base case results demonstrated that the highest ICER was in the U.S. ($571,000/QALY) and the lowest was in Australia ($277,000/QALY). In Canada, the U.K., and Israel, ICERs ranged between $351,000 and $358,000 per QALY. PSA demonstrated 0% likelihood of bevacizumab being cost-effective in any country at a willingness to pay threshold of $150,000 per QALY. CONCLUSION: The addition of bevacizumab to first-line chemotherapy for mCRC consistently fails to be cost-effective in all five countries. There are large differences in cost-effectiveness between countries. This study provides a framework for analyzing the value of a cancer drug from the perspectives of multiple international payers. IMPLICATIONS FOR PRACTICE: The cost-effectiveness of bevacizumab varies significantly between multiple countries. By conventional thresholds, bevacizumab is not cost-effective in metastatic colon cancer in the U.S., the U.K., Australia, Canada, and Israel.
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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 | 0.001 | 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) | 0.000 | 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".