Promotion and Leakage under a Pharmaceutical Price-Volume Agreement
Bibliographic record
Abstract
Third-party payers often reimburse drugs that are listed on formularies. Formularies list drugs that have been clinically proved to be safe and effective and have been approved for certain uses by a regulatory authority, such as the U.S. Food and Drug Administration. However, once a drug is approved, physicians may also prescribe it for unapproved or “off-label” indications. In addition, although third-party payers may specify some of labelled uses for reimbursement, prescriptions may leak to unspecified but labelled indications. Once a drug is listed on a formulary, the payer faces unlimited liability for that drug. Drug manufacturers thus try to get their drugs listed on a formulary and promote sales for both labelled and off-label uses. Some third-party payers use price-volume agreements to control unspecified drug uses. This paper investigates how a manufacturer would make marketing decisions under a price-volume agreement. We develop an optimization model in which the manufacturer maximizes its expected profit by choosing marketing efforts to promote different uses. We also compare models when off-label uses are reimbursed and when they are completely avoided to illustrate the impact of off-label promotion on the optimal decisions, on the decision makers' performance and on the cost-effectiveness of drug uses. We are not aware of any paper that theoretically investigates off-label promotions. This paper drives a number of interesting managerial insights on how to control off-label uses by applying operations research methods to address a health care policy issue.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".