Cost-effectiveness analysis of bevacizumab biosimilars for metastatic colorectal cancer: A comparative study using real-world data.
Bibliographic record
Abstract
9 Background: MVASI (Amgen) and Zirabev (Pfizer) are two of the earliest bevacizumab biosimilars approved for the first-line treatment of metastatic colorectal cancer (mCRC). While the introduction of biosimilars present an opportunity to alleviate the financial toxicity owing to the escalating costs of novel biologics, biosimilars should be comparatively assessed against the reference biologic in a real-world setting to confirm that they are indeed cost-saving or cost-effective after implementation. In this study, we aimed to confirm and quantify the real-world cost-savings of MVASI and Zirabev relative to originator bevacizumab (Avastin) for patients with mCRC. Methods: We conducted a population-based, retrospective cohort study in Ontario, Canada, where originator and biosimilar bevacizumab are universally publicly funded. Capturing the entire population of Ontario, we assessed all mCRC patients who received originator bevacizumab between January 1, 2008, and August 11, 2019, or biosimilar bevacizumab between August 12, 2019, and March 31, 2021. Biosimilar cases and originator bevacizumab controls were matched 1:4 using propensity score methods to adjust for differences at baseline. We calculated 1-year total patient-level costs (in Canadian dollars) and effects (in life years and quality-adjusted life years (QALY)) from the public health payer’s perspective. The primary outcomes for estimating cost-effectiveness were incremental net monetary benefit (INMB) and incremental net health benefit (INHB), calculated at willingness-to-pay (WTP) thresholds ranging from $50,000-200,000 per life year gained. Sensitivity analyses included a subgroup analysis by biosimilar type (MVASI/Zirabev) and an analysis using a 2-year time horizon. Results: The final propensity score matched cohort included 747 biosimilar cases and 2,945 controls. Bevacizumab biosimilars were associated with an incremental cost of -$6,379 (95% confidence interval (CI): -9,417, -3,537) (i.e., cost-saving) and an incremental effect of 0.0 (95% CI: -0.02, 0.02) life years gained and -0.01 (95% CI: -0.03, 0) QALY gained. INMB and INHB estimates indicated that biosimilar bevacizumab is cost-effective at all WTP thresholds assessed, with results remaining consistent across our biosimilar type subgroups and 2-year sensitivity analyses. Conclusions: Bevacizumab biosimilars demonstrated real-world cost-savings while providing similar survival benefit as originator bevacizumab, confirming the initial expectations of their implementation.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".