Information, Learning, and Drug Diffusion: the Case of Cox-2 Inhibitors

Pradeep Chintagunta, Ginger Jin and Renna Jiang, Quantitative Marketing and Economics 7(4), 399-443, December .


The recent withdrawal of Cox-2 Inhibitors has generated debate on the role of information in drug diffusion: can the market learn the efficacy of new drugs, or does it depend solely on manufacturer advertising and FDA updates? In this study, we use a novel data set to study the role of learning in the diffusion of three Cox-2 Inhibitors—Celebrex, Vioxx and Bextra—before the Vioxx withdrawal. Our study has two unique features: first, we observe each patient’s reported satisfaction after consuming a drug. This patient level data set, together with market level data on FDA updates, media coverage, academic articles, and pharmaceutical advertising, allows us to model individual prescription decisions. Second, we distinguish across-patient learning of a drug’s general efficacy from the within-patient learning of the match between a drug and a patient. Our results suggest that prescription choice is sensitive to many sources of information. At the beginning of 2001 and upon Bextra entry in January 2002, doctors held a strong prior belief about the efficacy of Celebrex, Vioxx, and Bextra. As a result, the learning from patient satisfaction is gradual and more concentrated on drug-patient match than on across-patient spillovers. News articles are weakly beneficial for Cox-2 drug sales, but academic articles appear to be detrimental. The impact of FDA updates is close to zero once we control for academic articles, which suggests that FDA updates follow academic articles and therefore deliver little new information to doctors. Two counterfactual experiments are carried out to quantify the influence of information on market shares.

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