The Effects of Detailing on Prescribing Decisions under Two-Sided Learning

Ching, Andrew and Ishihara, Masakazu (2007): The Effects of Detailing on Prescribing Decisions under Two-Sided Learning. Unpublished.

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 very 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.

• Item Type: MPRA Paper
• Institution: Rotman School of Management, University of Toronto
• Language: English
• Keywords: Detailing; Prescription Drugs; Decisions Under Uncertainty; Two-sided Learning; Representative Opinion Leader; Diffusion
• Subjects: I - Health, Education, and Welfare > I1 - Health > I18 - Government Policy; Regulation; Public Health; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C15 - Statistical Simulation Methods; Monte Carlo Methods; Bootstrap Methods; M - Business Administration and Business Economics; Marketing; Accounting > M3 - Marketing and Advertising > M31 - Marketing; D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D83 - Search; Learning; Information and Knowledge; Communication; Belief; M - Business Administration and Business Economics; Marketing; Accounting > M3 - Marketing and Advertising > M37 - Advertising; C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C35 - Discrete Regression and Qualitative Choice Models; Discrete Regressors; I - Health, Education, and Welfare > I1 - Health > I11 - Analysis of Health Care Markets