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QME

, Volume 8, Issue 2, pp 123–165 | Cite as

The effects of detailing on prescribing decisions under quality uncertainty

  • Andrew ChingEmail author
  • Masakazu Ishihara
Article

Abstract

Motivated by recent empirical findings on the relationship between new clinical evidence and the effectiveness of detailing, this paper develops a new structural model of detailing and prescribing decisions under the environment where both manufacturers and physicians are uncertain about drug qualities. Our model assumes (1) a representative opinion leader is responsible for updating the prior belief about the quality of drugs via consumption experiences and clinical trial outcomes, and (2) manufacturers use detailing as a means to build/maintain the measure of physicians who are informed of the current information sets. Unlike previous learning models with informative detailing, our model directly links the effectiveness of detailing to the current information sets and the measures of well-informed physicians. To illustrate the empirical implications of the new model, we estimate our model using a product level panel data on sales volume, prices, detailing minutes, and clinical trial outcomes for ACE-inhibitors with diuretics in Canada. Using our estimates, we demonstrate how the effectiveness of detailing depends on the information sets and the measures of well-informed physicians. Furthermore, we conduct a policy experiment to examine how a public awareness campaign, which encourages physicians/patients to report their drug experiences, would affect managerial incentives to detail. The results demonstrate that the empirical and managerial implications of our model can be very different from those of previous models. We argue that our results point out the importance of developing a structural model that captures the mechanism of how detailing/advertising conveys information in the market under study.

Keywords

Detailing Prescription drugs Decisions under uncertainty Representative opinion leader Diffusion 

JEL Classification

D83 I11 I18 M31 M37 M38 

Notes

Acknowledgements

We thank Dan Ackerberg, Pradeep Chintagunta, Avi Goldfarb, Tom Holmes, Ig Horstmann, Ahmed Khwaja, Nitin Mehta, Sridhar Moorthy, Mengze Shi, Wei Tan, various conferences and seminars participants, and in particular two anonymous referees, and the editor, Peter Rossi, for their helpful comments. We also thank Matt Shum for helping us obtain the data from IMS Canada. The usual disclaimer applies. Ching’s work on this project is supported by Connaught New-staff matching grant at the University of Toronto.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Rotman School of ManagementUniversity of TorontoTorontoCanada

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