The application of pharmacoeconomic modelling to estimate a value‐based price for new cancer drugs
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Value-based pricing has recently been discussed by international bodies as a means to estimate a drug price that is linked to the benefits it offers patients and society. The World Health Organization (WHO) has recommended using three times a country's per capita gross domestic product (GDP) as the threshold for economic value. Using the WHO criteria, pharmacoeconomic modelling was used to illustrate the application of value-based price towards bevacizumab, a relatively new drug that provides a 1.4-month survival benefit to patients with metastatic colorectal cancer (mCRC). METHODS: A decision model was developed to simulate outcomes in mCRC patients receiving chemotherapy ± bevacizumab. Clinical data were obtained from randomized trials and costs from Canadian cancer centres. Utility estimates were determined by interviewing 24 oncology nurses and pharmacists. A price per dose of bevacizumab was then estimated using a target threshold of $CAD117,000 per quality adjusted life year gained, which is three times the Canadian per capita GDP. RESULTS: For a 1.4-month survival benefit, a price of $CAD830 per dose would be considered cost-effective from the Canadian public health care perspective. If the drug were able to improve patient quality of life or survival from 1.4 to 3 months, the drug price could increase to $CAD1560 and $CAD2180 and still be considered cost-effective. DISCUSSION: The use of the WHO criteria for estimating a value-based price is feasible, but a balance between what patients/governments can afford to pay and the commercial viability of the product in the reference country would be required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".