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The simultaneous valuation of states from multiple instruments using ranking and VAS data: methods and preliminary results


  • Rowen, D
  • Brazier, J
  • Tsuchiya, A
  • Hernández, M
  • Ibbotson, R


Background: Previous methods of empirical mapping involve using regressions on patient or general population self-report data from datasets involving two or more instruments. This approach relies on overlap in the descriptive systems of the measures, but key dimensions may not be present in both measures. Furthermore this assumes it is appropriate to use different instruments on the same population, which may not be the case for all patient groups. The aim of the study described here is to develop a new method of mapping using general population preferences for hypothetical health states defined by the descriptive systems of different measures. This paper presents a description of the methods used in the study and reports on the results of the valuation study including details about the respondents, feasibility and quality (e.g. response rate, completion and consistency) and descriptive results on VAS and ranking data. The use of these results to estimate mapping functions between instruments will be presented in a companion paper. Methods: The study used interviewer administered versions of ranking and VAS techniques to value 13 health states defined by each of 6 instruments: EQ-5D (generic), SF-6D (generic), HUI2 (generic for children), AQL-5D (asthma specific), OPUS (social care specific), ICECAP (capabilities). Each interview involved 3 ranking and visual analogue scale (VAS) tasks with states from 3 different instruments where each task involves the simultaneous valuation of multiple instruments. The study includes 13 health and well-being states for each instrument (16 for EQ-5D) that reflect a range of health state values according to the published health state values for each instrument and each health state is valued approximately 75-100 times. Results: The sample consists of 499 members of the UK general population with a reasonable spread of background characteristics (response rate=55%). The study achieved a completion rate of 99% for all states included in the rank and rating tasks and 94.8% of respondents have complete VAS responses and 97.2% have complete rank responses. Interviewers reported that it is doubtful for 4.1% of respondents that they understood the tasks, and 29.3% of respondents stated that they found the tasks difficult. The results suggest important differences in the range of mean VAS and mean rank values per state across instruments, for example mean VAS values for the worst state vary across instruments from 0.075 to 0.324. Respondents are able to change the ordering of states between the rank and VAS tasks and 12.0% of respondents have one or more differences in their rank and VAS orderings for every task. Conclusions: This study has demonstrated the feasibility of simultaneously valuing health states from different preference-based instruments. The preliminary analysis of the results presented here provides the basis for a new method of mapping between measures based on general population preferences.

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  • Rowen, D & Brazier, J & Tsuchiya, A & Hernández, M & Ibbotson, R, 2009. "The simultaneous valuation of states from multiple instruments using ranking and VAS data: methods and preliminary results," MPRA Paper 29841, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:29841

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    References listed on IDEAS

    1. Brazier, John & Ratcliffe, Julie & Salomon, Joshua & Tsuchiya, Aki, 2016. "Measuring and Valuing Health Benefits for Economic Evaluation," OUP Catalogue, Oxford University Press, edition 2, number 9780198725923.
    2. Drummond, Michael F. & Sculpher, Mark J. & Torrance, George W. & O'Brien, Bernie J. & Stoddart, Greg L., 2005. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 3, number 9780198529453.
    3. Tsuchiya, A & Brazier, J & McColl, E & Parkin, D, 2002. "Deriving preference-based single indices from non-preference based condition-specific instruments: converting AQLQ into EQ5D indices," MPRA Paper 29740, University Library of Munich, Germany.
    4. McCabe, Christopher & Brazier, John & Gilks, Peter & Tsuchiya, Aki & Roberts, Jennifer & O'Hagan, Anthony & Stevens, Katherine, 2006. "Using rank data to estimate health state utility models," Journal of Health Economics, Elsevier, vol. 25(3), pages 418-431, May.
    5. Brazier, John & Roberts, Jennifer & Deverill, Mark, 2002. "The estimation of a preference-based measure of health from the SF-36," Journal of Health Economics, Elsevier, vol. 21(2), pages 271-292, March.
    6. Christopher McCabe & Katherine Stevens & Jennifer Roberts & John Brazier, 2005. "Health state values for the HUI 2 descriptive system: results from a UK survey," Health Economics, John Wiley & Sons, Ltd., vol. 14(3), pages 231-244.
    7. Young, Tracey A. & Yang, Y & Brazier, J & Tsuchiya, A, 2007. "The use of Rasch analysis as a tool in the construction of a preference based measure: the case of AQLQ," MPRA Paper 29802, University Library of Munich, Germany.
    8. John Brazier & Jennifer Roberts & Aki Tsuchiya & Jan Busschbach, 2004. "A comparison of the EQ-5D and SF-6D across seven patient groups," Health Economics, John Wiley & Sons, Ltd., vol. 13(9), pages 873-884.
    9. Louise Longworth & Stirling Bryan, 2003. "An empirical comparison of EQ-5D and SF-6D in liver transplant patients," Health Economics, John Wiley & Sons, Ltd., vol. 12(12), pages 1061-1067.
    10. Bernie J. O'Brien & Marian Spath & Gordon Blackhouse & J.L. Severens & Paul Dorian & John Brazier, 2003. "A view from the bridge: agreement between the SF-6D utility algorithm and the Health Utilities Index," Health Economics, John Wiley & Sons, Ltd., vol. 12(11), pages 975-981.
    11. Ryan, Mandy & Netten, Ann & Skatun, Diane & Smith, Paul, 2006. "Using discrete choice experiments to estimate a preference-based measure of outcome--An application to social care for older people," Journal of Health Economics, Elsevier, vol. 25(5), pages 927-944, September.
    12. Coast, Joanna & Flynn, Terry N. & Natarajan, Lucy & Sproston, Kerry & Lewis, Jane & Louviere, Jordan J. & Peters, Tim J., 2008. "Valuing the ICECAP capability index for older people," Social Science & Medicine, Elsevier, vol. 67(5), pages 874-882, September.
    13. Tsuchiya, Aki & Brazier, John & Roberts, Jennifer, 2006. "Comparison of valuation methods used to generate the EQ-5D and the SF-6D value sets," Journal of Health Economics, Elsevier, vol. 25(2), pages 334-346, March.
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    More about this item


    preference-based measures of health; quality of life; mapping; visual analogue scale; ranking;

    JEL classification:

    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • I19 - Health, Education, and Welfare - - Health - - - Other


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