The Effects of Detailing on Prescribing Decisions under Two-Sided Learning
Bibliographic record
Abstract
A fundamental question in pharmaceutical marketing management is: How does the effectiveness of detailing change when additional information on drugs is revealed via patients' experiences during the product lifecycle? To address this question, we develop a model of detailing and prescribing decisions which incorporates uncertainty about the quality of drugs. Our model assumes that not only physicians/patients, but also drug manufacturers are uncertain about the qualities of drugs, and a representative opinion leader is responsible for updating the prior belief about these qualities. Physicians are heterogeneous in their information sets, and drug manufacturers use detailing as a means to increase/maintain the measure of well-informed physicians. We explicitly model physicians' forgetting by allowing the measure of well-informed physicians to depreciate over time. We estimate our model using product level data of ACE-inhibitor with diuretic in Canada. Our estimation approach allows us to control for the potential endogeneity of detailing. The results show that our model is able to fit the diffusion pattern\nvery well, the effectiveness of detailing depends on the current information set and the measure of well-informed physicians, and the role of detailing-in-utility is minimal. Using our parameter estimates, we examine how a public awareness campaign, which encourages physicians/patients to report their drug experiences, would affect managerial incentives to detail.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".