Offering Pharmaceutical Samples: The Role of Physician Learning and Patient Payment Ability
Bibliographic record
Abstract
Physicians may learn about prescription drug effectiveness directly from the firm via detailing or from patient experience. Patient-mediated learning is aided by the use of free drug samples. The effective use of samples is hampered by a lack of understanding of its exact return on investment implications. We seek to fill this gap by incorporating the physician's sample allocation behavior in the firm's decision making. We uncover the following implications for firms as well as policy makers. First, we find that the optimal sampling level for a drug category is a nonmonotonic function of patient payment ability and the price of the drug. Second, an increase in the cost of samples can lead to an increase in sampling and a decrease in detailing when the physician's propensity to provide sample subsidies is high. Third, when future market growth is expected to be high (early stage product life cycle and/or chronic drugs) and sampling efficiency is low, the use of sampling is profitable for the firm but leads to lower market coverage than when sampling is disallowed.
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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 | 0.007 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".