Drug Detailing and Doctors' Prescription Decisions: The Role of Information Content in the Face of Competitive Entry

We study the effects of information content in 59,814 pharmaceutical sales calls on doctors' prescription decisions for statins, in the face of entry of competing brands and generics, using a hierarchical Bayesian distributed lag model. We conclude that adding information content to the prescription response model improves the in- and out-of-sample performance of the model. In the first six months following generic entry, it is more effective for incumbent brands to detail on drug contraindications and indications, compared to other periods, to positively differentiate from generics. In the first six months following branded entry, it is less effective for incumbent brands to detail on drug indications and costs, given increased competitive clutter. We also document substantial heterogeneity among doctors in their response to information content. Our model is helpful for analysts to more accurately assess the effectiveness of detailing. Our empirical results are also informative for drug manufacturers as they set or change their messaging policies in response to entry and help firms to tailor their message content at the doctor level.