Modeling competition among pharmaceutical drugs

Competition between rival brands within the same category gives rise to special competition/substitution effects of great interest to the involved forms. The study of competition in the pharmaceutical market highlights special behavior in the diffusion of knowledge about the products that may differ from other competing arenas. This latent feature naturally affects the evolution of the drugs' performances, in terms of number of packages sold. The aim of this paper is to propose a new model structure within the family of innovation diffusion models that specifically takes the step of knowledge spread into account. We show the application of this model with nonlinear regression methods and a comparison with alternative models to antidiabetic drug sales recorded monthly in the Italian market.

[1]  Guillermo Abramson,et al.  Statistics of extinction and survival in Lotka-Volterra systems , 1998, adap-org/9805001.

[2]  Cinzia Mortarino,et al.  Multivariate nonlinear least squares: robustness and efficiency of standard versus Beauchamp and Cornell methodologies , 2014, Comput. Stat..

[3]  Cinzia Mortarino,et al.  Sequential market entries and competition modelling in multi-innovation diffusions , 2012, Eur. J. Oper. Res..

[4]  Cinzia Mortarino,et al.  Within-brand and cross-brand word-of-mouth for sequential multi-innovation diffusions , 2014 .

[5]  Frank M. Bass,et al.  Impact of a Late Entrant on the Diffusion of a New Product/Service , 2000 .

[6]  Jesse H. Ausubel,et al.  Carrying Capacity: A Model with Logistically Varying Limits , 1999 .

[7]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[8]  Aldo Goia,et al.  Demographic processes in a model of innovation diffusion with dynamic market , 2007 .

[9]  Dipak C. Jain,et al.  Why the Bass Model Fits without Decision Variables , 1994 .

[10]  R. G. Cornell,et al.  Simultaneous Nonlinear Estimation , 1966 .

[11]  Mariangela Guidolin,et al.  Modelling a dynamic market potential: A class of automata networks for diffusion of innovations , 2009 .

[12]  V. Mahajan,et al.  Innovation diffusion and new product growth models: A critical review and research directions , 2010 .

[13]  C. Furlan,et al.  Pleural mesothelioma: forecasts of the death toll in the area of Casale Monferrato, Italy , 2012, Statistics in medicine.

[14]  Christian Terwiesch,et al.  Optimal Product Launch Times in a Duopoly: Balancing Life-Cycle Revenues with Product Cost , 2005, Oper. Res..

[15]  A. Valle,et al.  World oil depletion Models : Price effects compared with strategic or technological interventions , 2007 .

[16]  Mark S Handcock,et al.  MODELING SOCIAL NETWORKS FROM SAMPLED DATA. , 2010, The annals of applied statistics.

[17]  M. N. Sharif,et al.  Binomial innovation diffusion models with dynamic potential adopter population , 1981 .

[18]  Namwoon Kim,et al.  A simultaneous model for innovative product categorysales diffusion and competitive dynamics , 1999 .

[19]  N. Meade,et al.  Modelling and forecasting the diffusion of innovation – A 25-year review , 2006 .