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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".