Modeling choice behavior for new pharmaceutical products.
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.
Bingham
M F
MF
Triangle Economic Research, Durham, NC, USA. mfb@ter.com
Johnson
F R
FR
Miller
D
D
D000700:Analgesics / Q000191:economics / Q000627:therapeutic use; D001294:Attitude to Health; D001499:Bayes Theorem; D002755:Choice Behavior; D003258:Consumer Behavior / Q000191:economics* / Q000706:statistics & numerical data; D003657:Decision Making; D003661:Decision Support Techniques; D017850:Economics, Pharmaceutical; D006301:Health Services Needs and Demand / Q000191:economics*; D006302:Health Services Research; D006801:Humans; D008389:Marketing of Health Services; D008390:Markov Chains; D008881:Migraine Disorders / Q000188:drug therapy; D015233:Models, Statistical; D019057:Quality-Adjusted Life Years
D000700:Analgesics