Modeling choice behavior for new pharmaceutical products.

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Bingham, MF; Johnson, FR; Miller, D

Published Date

  • January 2001

Published In

Volume / Issue

  • 4 / 1

Start / End Page

  • 32 - 44

PubMed ID

  • 11704970

Electronic International Standard Serial Number (EISSN)

  • 1524-4733

International Standard Serial Number (ISSN)

  • 1098-3015

Digital Object Identifier (DOI)

  • 10.1046/j.1524-4733.2001.004001032.x

Language

  • eng