Bevacizumab (bev) for metastatic colorectal cancer (mCRC): A global cost-effectiveness analysis.
Bibliographic record
Abstract
6518 Background: In the United States, the addition of bev to 1st-line chemotherapy in mCRC provides an additional 0.10 quality-adjusted life years (QALYs) at an incremental cost-effectiveness ratio (ICER) of $571,240/QALY (Goldstein et al. Journal of Clinical Oncology, 2015). However, this estimate of value is not transferrable between countries due to variable international pricing strategies. Our objective was to establish the cost-effectiveness of bev in mCRC in 5 different countries: the US, the UK, Canada (CAN), Australia (AUS), and Israel (ISR). Methods: We performed the analysis on the basis of a previously published Markov model (Goldstein et al. JCO, 2015). Input data for efficacy, adverse events and quality of life were considered to be standard for all countries. We used country specific prices for all healthcare services, medications, and administration costs. For the UK, all costs were obtained from UK Department of Health, National Health Service and British National Formulary. For CAN, all costs were obtained from the Ontario Drug Benefit formulary, Ontario Schedule of Benefits, and Sunnybrook Pharmacy Stores Department. For AUS, all costs were obtained from the Pharmaceutical Benefits Scheme and the Australian Medicare Benefits Schedule. For ISR, all costs were obtained from the Israeli Ministry for Health and Clalit Health Services. All costs were converted from local currency to US dollars in December 2015. Probabilistic sensitivity analyses (PSA) assessed the model robustness against the parameter uncertainties. Results: Base case results are displayed in the table. PSA demonstrated 0% likelihood of bev being cost cost-effective in any country at a willingness to pay threshold of $150,000 per QALY. Conclusions: Although there are differences in the value of bev between countries, the addition of bev to first-line chemotherapy for mCRC is not cost-effective across all 5 countries analyzed. This study provides a novel framework for analyzing the value of a cancer drug with a single model from the perspectives of multiple international payers. USA UK Canada Australia Israel Total incremental cost (2015 US$) 60,551 33,253 35,698 29,512 37,995 ICER (US$/QALY) 571,240 313,704 336,778 278,417 358,445
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".