Optimal drug pricing, limited use conditions and stratified net benefits for Markov models of disease progression
Bibliographic record
Abstract
Limited use conditions (LUCs) are a method of directing treatment with new drugs to those populations where they will be most cost effective. In this paper we investigate how a drug manufacturer could determine pricing and LUCs to maximize profits. We assume that the payer makes formulary decisions on the basis of net monetary benefits, that the disease can be modeled using a Markov model of disease progression, and that the drug reduces the probability of progression between states of the Markov model. LUCs are expressed as a range of probabilities of disease progression over which patients would have access to the new drug. We assume that the manufacturer determines the price and LUCs in order to maximize profits. We show that an explicit trade-off exists between the drug's price and the use conditions, that there is an upper bound on the drug price, that the proportion of the population targeted by the LUC does not depend on quality of life or costs in each health state or the payer's willingness to pay, and that high drug prices do not always correspond with high profits.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".