Real-World Cost-Effectiveness of Bevacizumab With First-Line Combination Chemotherapy in Patients With Metastatic Colorectal Cancer: Population-Based Retrospective Cohort Studies in Three Canadian Provinces
Bibliographic record
Abstract
Background. Real-world evidence can be a valuable tool when clinical trial data are incomplete or uncertain. Bevacizumab was adopted as first-line therapy for metastatic colorectal cancer (mCRC) based on significant survival improvements in initial clinical trials; however, survival benefit diminished in subsequent analyses. Consequently, there is uncertainty surrounding the cost-effectiveness of bevacizumab therapy achieved in practice. Objective. To assess real-world cost-effectiveness of first-line bevacizumab with irinotecan-based chemotherapy versus irinotecan-based chemotherapy alone for mCRC in British Columbia (BC), Saskatchewan, and Ontario, Canada. Methods. Using provincial cancer registries and linked administrative databases, we identified mCRC patients who initiated publicly funded irinotecan-based chemotherapy, with or without bevacizumab, in 2000 to 2015. We compared bevacizumab-treated patients to historical controls (treated before bevacizumab funding) and contemporaneous controls (receiving chemotherapy without bevacizumab), using inverse-probability-of-treatment weighting with propensity scores to balance baseline covariates. We calculated incremental cost-effectiveness ratios (ICER) using 5-year cost and survival adjusted for censoring, with bootstrapping to characterize uncertainty. We also conducted one-way sensitivity analysis for key drivers of cost-effectiveness. Results. The cohorts included 12,112 (Ontario), 1,161 (Saskatchewan), and 2,977 (BC) patients. Bevacizumab significantly increased treatment costs, with mean ICERs between $78,000 and $84,000/LYG (life-year gained) in the contemporaneous comparisons and $75,000 and $101,000/LYG in the historical comparisons. Reducing the cost of bevacizumab by 50% brought ICERs in all comparisons below $61,000/LYG. Limitations. Residual confounding in observational data may bias results, while the use of original list prices overestimates current bevacizumab cost. Conclusion. The addition of bevacizumab to irinotecan-based chemotherapy extended survival for mCRC patients but at significant cost. At original list prices bevacizumab can only be considered cost-effective with certainty at a willingness-to-pay threshold over $100,000/LYG, but price reductions or discounts have a significant impact on cost-effectiveness.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".