Pricing and Promotional Decisions of a Drug Manufacturer with the Presence of a Price-Volume Agreement
Bibliographic record
Abstract
Physicians prescribe not only based on the health outcome and costs of a drug, but also based on their personal experience and preference for certain drugs. Thus, drug manufacturers invest a significant amount of promotional efforts to influence physicians, efforts including detailing, invited conferences, and sponsored research, etc. Since a third-party payer must pay for all claims for a drug that is covered by an insurance plan, the expense for the drug may be very high and uncertain. To control increasing drug costs and sales uncertainty, price-volume agreements have been proposed as a way to reduce the risk of higher-than-expected drug expenses. In such agreements, a drug manufacturer has to return a portion to the payer of sales exceeding a pre-specified volume threshold. In this paper, we investigated the drug pricing and promotional decisions of a drug manufacturer in the presence of a price-volume agreement under three different pricing scenarios. We found it not necessarily true that a high drug price or a high quality drug (with a high health benefit) reduces the manufacturer’s motivation to promote. We also found that the existence of a price-volume agreement may not increase the drug price, but it does help control the promotional effort. Although a negotiated price may be lower than the price set by the manufacturer, a negotiation may not always be preferred because the manufacturer’s profit is reduced by the negotiation and thus an agreement may not be reached under certain circumstances.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".