A Structural Model of Correlated Learning and Late-Mover Advantages: The Case of Statins



When Lipitor entered the statin (a class of anti-cholesterol drugs) market in 1997, some incumbent drugs had already obtained strong clinical evidence to show their efficacy in preventing heart diseases.  However, despite its lack of such important evidence, Lipitor quickly became the most commonly used statin among new patients.  To explain this puzzle, we propose a theory of correlated learning and indirect inference by physicians.  We introduce a concept of ``efficiency ratio'', which measures how efficiently a drug can convert reduction in cholesterol levels to reduction in heart disease risks.  We assume physicians are uncertain about drugs' efficiency ratios, and allow the physicians' initial prior belief to be correlated across drugs.  With correlated prior perceptions, a new clinical trial's information on a drug's efficiency ratio can update physicians' belief on other statins' efficiency ratios.  Physicians then infer each statin's ability in reducing heart disease risks based on its perceived efficiency ratio and its ability in reducing cholesterol.  Consequently, correlated learning may allow late entrants to gain late-mover advantages by free-riding on the clinical evidence and informative marketing activities of the incumbents.  To estimate our model, we use a data set on market shares, patients' switching rates and discontinuing rates, as well as detailing and media coverage from 1993 to 2004.  Our estimation results shows that correlated learning about statins' efficiency ratios is strong.  This, together with the fact that two late entrants, Lipitor and Crestor, are more effective in lowering cholesterol levels, allow them to gain late-mover advantages.  Moreover, we find that intensive detailing efforts (via its informative and persuasive roles) also contribute to their successes.