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.
Duke Scholars
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Related Subject Headings
- Quality-Adjusted Life Years
- Models, Statistical
- Migraine Disorders
- Markov Chains
- Marketing of Health Services
- Humans
- Health Services Research
- Health Services Needs and Demand
- Health Policy & Services
- Economics, Pharmaceutical
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Quality-Adjusted Life Years
- Models, Statistical
- Migraine Disorders
- Markov Chains
- Marketing of Health Services
- Humans
- Health Services Research
- Health Services Needs and Demand
- Health Policy & Services
- Economics, Pharmaceutical