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A Closer Look at Decision and Analyst Error by Including Nonlinearities in Discrete Choice Models: Implications on Willingness-to-Pay Estimates Derived from Discrete Choice Data in Healthcare

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  • Esther Bekker-Grob
  • John Rose
  • Michiel Bliemer

Abstract

Complex indirect utility functions reduce error arisen from researchers, which can have important implications for measures in healthcare such as the WTP, whereas EMNL provides insights into the behaviour of respondents when answering DCEs. Understanding how respondents answer DCE questions may allow researchers to construct DCEs that minimise scale differences, so that the decision error made across respondents is more homogeneous and therefore taken out as additional noise in the data. Hence, better estimates of preferences in healthcare can be provided. Copyright Springer International Publishing Switzerland 2013

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  • Esther Bekker-Grob & John Rose & Michiel Bliemer, 2013. "A Closer Look at Decision and Analyst Error by Including Nonlinearities in Discrete Choice Models: Implications on Willingness-to-Pay Estimates Derived from Discrete Choice Data in Healthcare," PharmacoEconomics, Springer, vol. 31(12), pages 1169-1183, December.
    • Handle: RePEc:spr:pharme:v:31:y:2013:i:12:p:1169-1183
    DOI: 10.1007/s40273-013-0100-3
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    References listed on IDEAS

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    2. Bansback, Nick & Hole, Arne Risa & Mulhern, Brendan & Tsuchiya, Aki, 2014. "Testing a discrete choice experiment including duration to value health states for large descriptive systems: Addressing design and sampling issues," Social Science & Medicine, Elsevier, vol. 114(C), pages 38-48.

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