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Modelling the EuroQol data: a comparison of discrete choice conjoint and conditional preference modelling

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Author Info

  • Zafar Hakim

    (Global Pharmacoeconomic Research, Roche Pharmaceuticals, Palo Alto, CA, USA)

  • Dev S. Pathak

    (College of Pharmacy and Centre for Health Outcomes, Policy, and Evaluation Studies, The Ohio State University, Columbus, OH, USA)

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    Abstract

    This article compares two measurement strategies for measuring EuroQol health state preferences: (a) conditional preference modelling, implemented using rating scale and standard gamble scaling methods and (b) discrete choice conjoint modelling. The nature of the model form of the EuroQol health status preference function and the predictive ability of each measurement strategy formed the basis of the comparison. Data were collected via personal interviews with 140 US patients, 139 of whom provided usable responses. Both strategies supported a multiplicative model form as representative of the EuroQol health status preference function and were acceptable in terms of predictive ability. The agreement of the two measurement strategies on the nature of the model form provides evidence of the convergent validity of the multiplicative nature of the EuroQol health status preference function in this patient population. Since both strategies were found to be acceptable in terms of predictive ability, further research comparing preference scores and measuring respondent evaluations of the methodologies is necessary to illustrate the relative strengths and weaknesses of different health state preference measurement methodologies. Copyright © 1999 John Wiley & Sons, Ltd.

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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

    Volume (Year): 8 (1999)
    Issue (Month): 2 ()
    Pages: 103-116

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    Handle: RePEc:wly:hlthec:v:8:y:1999:i:2:p:103-116

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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    References

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    1. Louviere, Jordan J & Hensher, David A, 1983. " Using Discrete Choice Models with Experimental Design Data to Forecast Consumer Demand for a Unique Cultural Event," Journal of Consumer Research, University of Chicago Press, vol. 10(3), pages 348-61, December.
    2. Paul Dolan & Claire Gudex & Paul Kind & Alan Williams, 1995. "A social tariff for EuroQol: results from a UK general population survey," Working Papers 138chedp, Centre for Health Economics, University of York.
    3. William Furlong & David Feeny & George Torrance & Ronald Barr & John Horsman, 1992. "Guide to Design and Development of Health-State Utility Instrumentation," Centre for Health Economics and Policy Analysis Working Paper Series 1990-09, Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Canada.
    4. Torrance, George W., 1986. "Measurement of health state utilities for economic appraisal : A review," Journal of Health Economics, Elsevier, vol. 5(1), pages 1-30, March.
    5. Kathryn Blackmond Laskey & Gregory W. Fischer, 1987. "Estimating Utility Functions in the Presence of Response Error," Management Science, INFORMS, vol. 33(8), pages 965-980, August.
    6. Nord, Erik, 1992. "Methods for quality adjustment of life years," Social Science & Medicine, Elsevier, vol. 34(5), pages 559-569, March.
    7. G Torrance & Y Zhang & D Feeny & W Furlong & R Barr, 1992. "Multi-attribute Utility Functions for a Comprehensive Health Status Classification System: Health Utilities Index Mark 2," Centre for Health Economics and Policy Analysis Working Paper Series 1992-18, Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Canada.
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    Cited by:
    1. Bansback, Nick & Brazier, John & Tsuchiya, Aki & Anis, Aslam, 2012. "Using a discrete choice experiment to estimate health state utility values," Journal of Health Economics, Elsevier, vol. 31(1), pages 306-318.
    2. Brazier, J & Rowen, D & Yang, Y & Tsuchiya, A, 2009. "Using rank and discrete choice data to estimate health state utility values on the QALY scale," MPRA Paper 29891, University Library of Munich, Germany.
    3. F. Reed Johnson & Melissa Ruby Banzhaf & William H. Desvousges, 2000. "Willingness to pay for improved respiratory and cardiovascular health: a multiple-format, stated-preference approach," Health Economics, John Wiley & Sons, Ltd., vol. 9(4), pages 295-317.
    4. McKenzie, Lynda & Cairns, John & Osman, Liesl, 2001. "Symptom-based outcome measures for asthma: the use of discrete choice methods to assess patient preferences," Health Policy, Elsevier, vol. 57(3), pages 193-204, September.
    5. Rosalie Viney & Elizabeth Savage & Jordan Louviere, 2005. "Empirical investigation of experimental design properties of discrete choice experiments in health care," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 349-362.
    6. John Brazier & Donna Rowen & Yaling Yang & Aki Tsuchiya, 2012. "Comparison of health state utility values derived using time trade-off, rank and discrete choice data anchored on the full health-dead scale," The European Journal of Health Economics, Springer, vol. 13(5), pages 575-587, October.
    7. Kara Hanson & Barbara McPake & Pamela Nakamba & Luke Archard, 2005. "Preferences for hospital quality in Zambia: results from a discrete choice experiment," Health Economics, John Wiley & Sons, Ltd., vol. 14(7), pages 687-701.
    8. Julie Ratcliffe & John Brazier & Aki Tsuchiya & Tara Symonds & Martin Brown, 2009. "Using DCE and ranking data to estimate cardinal values for health states for deriving a preference-based single index from the sexual quality of life questionnaire," Health Economics, John Wiley & Sons, Ltd., vol. 18(11), pages 1261-1276.

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