Modeling choice behavior for new pharmaceutical products

Value Health. Jan-Feb 2001;4(1):32-44. doi: 10.1046/j.1524-4733.2001.004001032.x.


This paper presents a dynamic generalization of a model often used to aid marketing decisions relating to conventional products. The model uses stated-preference data in a random-utility framework to predict adoption rates for new pharmaceutical products. In addition, this paper employs a Markov model of patient learning in drug selection. While the simple learning rule presented here is only a rough approximation to reality, this model nevertheless systematically incorporates important features including learning and the influence of shifting preferences on market share. Despite its simplifications, the integrated framework of random-utility and product attribute updating presented here is capable of accommodating a variety of pharmaceutical marketing and development problems. This research demonstrates both the strengths of stated-preference market research and some of its shortcomings for pharmaceutical applications.

MeSH terms

  • Analgesics / economics
  • Analgesics / therapeutic use
  • Attitude to Health
  • Bayes Theorem
  • Choice Behavior*
  • Consumer Behavior / economics*
  • Consumer Behavior / statistics & numerical data
  • Decision Making
  • Decision Support Techniques*
  • Economics, Pharmaceutical*
  • Health Services Needs and Demand / economics*
  • Health Services Research
  • Humans
  • Marketing of Health Services
  • Markov Chains
  • Migraine Disorders / drug therapy
  • Models, Statistical
  • Quality-Adjusted Life Years


  • Analgesics