New Drug Diffusion When Forward-Looking Physicians Learn from Patient Feedback and Detailing

Abstract

We study physicians' prescription choices when uncertainty about drug efficacy is resolved through two channels: firms' marketing activities (e.g., detailing) and patients' experiences with the drugs. We first provide empirical evidence that suggests the well-understood information incentive for physicians to experiment with new drugs is reduced when physicians anticipate future detailing. Increased detailing activity therefore triggers opposing forces: adoption is hastened as physicians become informed (assuming priors are initially low), and slows as they reduce experimentation and instead obtain information from detailing at no cost. We then estimate a dynamic Bayesian learning model that embodies these trade-offs using physician-level data on prescription choices and detailing received in the months surrounding the introduction of two erectile dysfunction drugs, Levitra and Cialis. Detailing elasticities are lower when physicians anticipate changes in detailing activity than when such changes are unexpected. Accordingly, to maximize the effect of detailing, firms should avoid announcing increases in detailing activities.

Keywords: uncertainty, learning, dynamic discrete choice, new drug diffusion

JEL Classification: D12, L21, M31