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 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".