Measuring the Informative and Persuasive Roles of Detailing on Prescribing Decisions
Bibliographic record
Abstract
In the pharmaceutical industry, measuring the importance of informative and persuasive roles of detailing is crucial for both drug manufacturers and policy makers. However, little progress has been made in disentangling these two roles of detailing in empirical research. In this paper, we provide a new identification strategy to address this problem. Our key identification assumptions are that the informative component of detailing is chemical specific and the persuasive component is brand specific. Our strategy is to focus on markets where some drug manufacturers engage in a comarketing agreement, under which two or more companies market the same chemical using their own brand names. With our identification assumptions, the variation in the relative market shares of these two brands, together with their brand specific detailing efforts, would allow us to measure the persuasive component of detailing. The variation in the market shares of chemicals, and the detailing efforts summed across brands made of the same chemical, would allow us to measure the informative component of detailing. Using the data for angiotensin-converting enzyme inhibitor with diuretic in Canada, we find evidence that our identification strategy can help disentangle these two effects. Although both effects are statistically significant, we find that the persuasive function of detailing plays a very minor role in determining the demand at the chemical level—the informative role of detailing is mainly responsible for the diffusion patterns of chemicals. In contrast, the persuasive role of detailing plays a crucial role in determining the demand for brands that comarket the same chemical. This paper was accepted by Pradeep Chintagunta, marketing.
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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 | 0.027 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".