Value‐based pricing: Toward achieving a balance between individual and population gains in health benefits
Bibliographic record
Abstract
OBJECTIVES: Value-based pricing of oncology drugs provides a best estimate for the price of a drug, as it relates to the benefits it provides for individual patients. To date, the impact of value-based pricing to reference cost-effectiveness thresholds (λ) on individual and population-level health benefits remains uncharacterized. The current study examined the potential benefits of value-based pricing by quantifying the incremental net health benefit (INHB) of publicly funded oncology drugs, if funding occurred at manufacturer-submitted price without value-based pricing. METHODS: Pan-Canadian Oncology Drug Review (pCODR) submissions were reviewed to identify eligible drug indications from which final economic guidance panel reports were reviewed for incremental costs (ΔC) and quality-adjusted life-years (ΔQALY) from manufacturer-submitted, pCODR lower-limit (pCODR-LL) and upper-limit (pCODR-UL) re-analyzed estimates. Annual number of cases in Ontario for each drug indication was obtained from population databases. Annual QALY gain per drug indication was determined by (ΔQALY × cases). Population QALY gain/loss in the absence of value-based pricing to reference λ was estimated by the INHB: (INHB = [ΔQALY - (ΔC/λ)] × cases). RESULTS: In total, 34 drug indications (4629 cases) were identified. Annual gain in QALYs for the funded drug indications using manufacturer, pCODR-LL, and pCODR-UL estimates was 1851, 1617, and 1301, respectively. At a λ $100 000/QALY, funding in the absence of value-based pricing resulted in loss of 2311, 2519, and 2604 QALYs. This would result in a provincial net annual loss of 460, 902, and 1303 QALYs. CONCLUSIONS: Despite an annual gain in QALY per funded drug indication, a net loss in QALY for the province, in the absence of value-based pricing, was demonstrated. Supportive evidence exists for value-based pricing toward the promotion of health benefits for the greater population.
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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 | 0.041 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".