Economic analysis of bevacizumab, cetuximab, and panitumumab with fluoropyrimidine-based chemotherapy in the first-line treatment of KRAS wild-type metastatic colorectal cancer (mCRC)
Bibliographic record
Abstract
OBJECTIVE: Colorectal cancer (CRC) is the third most commonly diagnosed cancer in Canada (excluding non-melanoma skin cancers). Bevacizumab is a recombinant humanized monoclonal antibody that selectively binds to human vascular endothelial growth factor. A sub-study confirmed its effectiveness in KRAS wild-type patients. Recent evidence has shown clinical benefit from anti-epidermal growth factor treatments cetuximab and panitumumab in these patients. The cost-effectiveness, to the Canadian healthcare system, of fluoropyrimidine-based chemotherapy (FBC) in combination with bevacizumab, cetuximab, or panitumumab was assessed for first-line treatment of KRAS wild-type mCRC patients. METHODS: A Markov model was developed and calibrated to progression-free/overall survival, using separately reported trial survival and adverse event results for each comparator. Health-state resource utilization was derived from published data and oncologist input. Utilities and unit prices were obtained from published literature and standard Canadian sources. RESULTS: Results per patient are over a lifetime horizon, to a maximum of 10 years, with 5% annual discounting. Comparators are ordered by total cost and the incremental cost-effectiveness ratio (ICER) of each is determined against the previous non-dominated therapy. Compared to FBC alone, bevacizumab + FBC has an ICER of $131,600 per QALY gained. Compared to bevacizumab + FBC, panitumumab + FBC is dominated and cetuximab + FBC has an ICER of $3.8 million per QALY. In probabilistic sensitivity analysis, bevacizumab + FBC had ∼100%, ∼100%, and 98.9% probabilities of being more cost-effective than both of the other combination treatments at thresholds of $50,000/QALY, $100,000/QALY, and $200,000/QALY, respectively. CONCLUSION: For first-line treatment of KRAS-WT mCRC, bevacizumab + FBC is associated with substantially lower costs as compared to panitumumab + FBC or cetuximab + FBC. Key limitations were that survival curves and adverse event rates were taken from separate clinical trials and that an indirect comparison was not included. Given these findings, bevacizumab is likely to offer the best value for money for this patient population.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".