Financial risk-sharing agreements: Using options to make marginal benefits cost effective.
Bibliographic record
Abstract
41 Background: New cancer treatments have become increasingly costly. Although new drugs may be sufficiently cost-efficiency for a primary indication (i1) to receive funding, subsequent indications (ia) may not prove cost-effective. A financial risk-sharing agreement to increase access is proposed. A potential 2012 real-world implementation in Canada for pazopanib use in metastatic renal cell carcinoma (mRCC) as i1, and soft-tissue sarcoma (STS) as ia is presented. Methods: The market value of an indication can be approximated by its net-present-value (NPV) of expected cash flows. If the incremental cost-effectiveness ratio (ICER) for ia is expected to be greater than willingness-to-pay (WTP), the payer then has an incentive to enter into a risk-sharing agreement. A binomial option pricing model is employed to compute the price of the call option to contract such an agreement. The probability of being eligible for ia is extracted from the pan-Canadian Oncology Drug Review (pCODR), a national drug funding advisory body. Correspondence with industry was performed to identify additional ia. Cost, effectiveness, the ICER, discount rates, and drug revenues are calculated using inputs from pCODR, the Bank of Canada, Statistics Canada, and pazopanib’s relevant clinical data at the time of its submission for i1. Results: Of the 47 drugs eligible for i1, 46% are eligible for an ia(Wilson interval 33%-60%). For pazopanib in mRCC, at a mean price of $164/day and an ICER of $0-$57,309, the expected NPV was computed to be $338M. In STS, based on its clinical data at the time, at the same price, the ICER was expected to be > $100,000, with an NPV of $37M. The price of the call option is $17M. Alternatively, the price of pazopanib could be adjusted to $172/day in mRCC, keeping its ICER below WTP, with an agreement that if efficacious, the drug would be provided by the company gratis for STS patients. Conclusions: In a reality where high drug costs are limiting access for vulnerable patients, risk sharing agreements using financial instruments offer an opportunity to re-deploy health technology assessment to expand drug access. For a price increase of $8 per day in i1, Canadian STS patients could have had access to an effective treatment instead of nothing.
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".