Cost-Effectiveness of First-Line Treatments for Patients with Kras Wild-Type Metastatic Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: Combinations of chemotherapy regimens and monoclonal antibodies have been demonstrated to improve clinical outcomes in patients with metastatic colorectal cancer (mcrc). Although these combination treatment strategies are safe and effective in first-line treatment for mcrc, little is known about their economic consequences and resource allocation implications. In the present study, we evaluated the cost-effectiveness of bevacizumab plus folfiri, cetuximab plus folfiri, and panitumumab plus folfiri for patients with KRAS wild-type mcrc. METHODS: A Markov model simulated the lifetime patient outcomes and costs of each first-line treatment strategy and subsequent lines of treatment from the perspective of the health care payer in Ontario. The model was parameterized using data from the Ontario Cancer Registry, Ontario health administrative databases, and published randomized control trials. Patient outcomes were measured in quality-adjusted life years (qalys), and costs were measured in monetary terms. Costs and outcomes were both discounted at 5% and expressed in 2012 Canadian dollars. RESULTS: For mcrc patients with KRAS wild-type disease, the treatment strategy of bevacizumab plus folfiri was found to dominate the other two first-line treatment strategies. Sensitivity analyses revealed that the incremental cost-effectiveness ratio values were sensitive to the effectiveness of treatment, the costs of bevacizumab and cetuximab, and health utility values. CONCLUSIONS: Evidence from Ontario showed that bevacizumab plus folfiri is the cost-effective first-line treatment strategy for patients with KRAS wild-type mcrc. The panitumumab plus folfiri and cetuximab plus folfiri options were both dominated, but the cetuximab plus folfiri strategy must be further investigated given that, in the sensitivity analyses, the cost-effectiveness of that strategy was found to be superior to that of bevacizumab plus folfiri under certain ranges of parameter values.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".