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Design techniques for stated preference methods in health economics

Carlsson, F and Martinsson, Peter LU (2003) In Health Economics 12(4). p.281-294
Abstract
This paper discusses different design techniques for stated preference surveys in health economic applications. In particular, we focus on different design techniques, i.e. how to combine the attribute levels into alternatives and choice sets, for choice experiments. Design is a vital issue in choice experiments since the combination of alternatives in the choice sets will determine the degree of precision obtainable from the estimates and welfare measures. In this paper we compare orthogonal, cyclical and D-optimal designs, where the latter allows expectations about the true parameters to be included when creating the design. Moreover, we discuss how to obtain prior information on the parameters and how to conduct a sequential design... (More)
This paper discusses different design techniques for stated preference surveys in health economic applications. In particular, we focus on different design techniques, i.e. how to combine the attribute levels into alternatives and choice sets, for choice experiments. Design is a vital issue in choice experiments since the combination of alternatives in the choice sets will determine the degree of precision obtainable from the estimates and welfare measures. In this paper we compare orthogonal, cyclical and D-optimal designs, where the latter allows expectations about the true parameters to be included when creating the design. Moreover, we discuss how to obtain prior information on the parameters and how to conduct a sequential design procedure during the actual experiment in order to improve the precision in the estimates. The designs are evaluated according to their ability to predict the true marginal willingness to pay under different specifications of the utility function in Monte Carlo simulations. Our results suggest that the designs produce unbiased estimations, but orthogonal designs result in larger mean square error in comparison to D-optimal designs. This result is expected when using correct priors on the parameters in D-optimal designs. However, the simulations show that welfare measures are not very sensitive if the choice sets are generated from a D-optimal design with biased priors. Copyright (C) 2002 John Wiley Sons, Ltd. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
optimal design, choice experiments, health economics
in
Health Economics
volume
12
issue
4
pages
281 - 294
publisher
John Wiley & Sons
external identifiers
  • pmid:12652515
  • wos:000182130400003
  • scopus:0037390267
ISSN
1099-1050
DOI
10.1002/hec.729
language
English
LU publication?
yes
id
57c31663-543b-4920-8eba-2eca1cfd8370 (old id 313402)
date added to LUP
2007-08-27 14:27:06
date last changed
2018-09-23 03:35:34
@article{57c31663-543b-4920-8eba-2eca1cfd8370,
  abstract     = {This paper discusses different design techniques for stated preference surveys in health economic applications. In particular, we focus on different design techniques, i.e. how to combine the attribute levels into alternatives and choice sets, for choice experiments. Design is a vital issue in choice experiments since the combination of alternatives in the choice sets will determine the degree of precision obtainable from the estimates and welfare measures. In this paper we compare orthogonal, cyclical and D-optimal designs, where the latter allows expectations about the true parameters to be included when creating the design. Moreover, we discuss how to obtain prior information on the parameters and how to conduct a sequential design procedure during the actual experiment in order to improve the precision in the estimates. The designs are evaluated according to their ability to predict the true marginal willingness to pay under different specifications of the utility function in Monte Carlo simulations. Our results suggest that the designs produce unbiased estimations, but orthogonal designs result in larger mean square error in comparison to D-optimal designs. This result is expected when using correct priors on the parameters in D-optimal designs. However, the simulations show that welfare measures are not very sensitive if the choice sets are generated from a D-optimal design with biased priors. Copyright (C) 2002 John Wiley Sons, Ltd.},
  author       = {Carlsson, F and Martinsson, Peter},
  issn         = {1099-1050},
  keyword      = {optimal design,choice experiments,health economics},
  language     = {eng},
  number       = {4},
  pages        = {281--294},
  publisher    = {John Wiley & Sons},
  series       = {Health Economics},
  title        = {Design techniques for stated preference methods in health economics},
  url          = {http://dx.doi.org/10.1002/hec.729},
  volume       = {12},
  year         = {2003},
}