The development of a value-based pricing index for new drugs in metastatic colorectal cancer.
Bibliographic record
Abstract
6038 Background: Worldwide, prices for cancer drugs have been under downward pressure where several governments have mandated price cuts of branded and generic products. A better alternative to mandated price cuts would be the estimation of a launch price based on drug performance, cost effectiveness and a county’s ability to pay. In this study, the development of a global pricing index for new drugs that encompasses all of these attributes in patients with metastatic colorectal cancer (mCRC) is described. Methods: A pharmacoeconomic model was developed to simulate clinical outcomes in mCRC patients receiving standard chemotherapy with the addition of a “new drug” that improves survival by 1.4, 3 and 6 months respectively. Cost and health state utility data were obtained from cancer centers and oncology nurses (n=112) in Canada, Spain, India, South Africa and Malaysia. A price per dose was estimated for each survival increment using a target value threshold of three times the per capita GDP for each country, as recommended by the World Health Organization (WHO). Multivariable analysis was then used to develop the pricing index, which considers survival benefit, per capita GDP and income dispersion as measured by the Gini coefficient as predictor variables. Results: Higher survival benefits were associated with elevated drug prices, especially in wealthier countries such as Canada and Spain. For a nation like Argentina with a per capita GDP of $15,000 and a Gini coefficient of 51, the pricing index estimated that for a drug which provides a 4 month survival benefit in mCRC, the value based price would be $U.S.630 per dose. In contrast, the same drug in a wealthier country like Norway could command a price of $U.S.2,775 and still be considered cost effective according to the WHO criteria. Conclusions: We present a global pricing index that can be used to estimate a value based price in different countries for new drugs in mCRC. The application of this index to estimate a price based on cost effectiveness would be a good starting point for opening dialogue between the key stakeholders and a better alternative to governments’ mandated price cuts.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".