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Mapping the EQ-5D Index from the SF-12: US General Population Preferences in a Nationally Representative Sample

Author

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  • Patrick W. Sullivan

    (University of Colorado School of Pharmacy, Pharmaceutical Outcomes Research Program, 4200 East Ninth Avenue, Box C238, Denver, CO 80262; telephone: (303) 315-1560; fax: (303) 315-1797; Patrick.Sullivan@UCHSC.edu.)

  • Vahram Ghushchyan

    (University of Colorado School of Pharmacy, Pharmaceutical Outcomes Research Program Denver, Colorado)

Abstract

Background Health status measures provide a numeric score representing a profile of health status across several domains, such as physical and mental health. The SF-12 and its longer form, the SF-36, are examples of generic health status measures. Although these Background . Previous mapping algorithms estimating EQ-5D index scores from the SF-12 were based on preferences from a UK community sample. However, preferences based on the general US population are most appropriate for costeffectiveness analyses done from the societal perspective in the United States. Objective . To provide a mapping algorithm for estimating EQ-5D index scores from the SF-12 based on a nationally representative sample and using preferences based on the general US population. Methods . The Medical Expenditure Panel Survey (MEPS) 2002 and 2000 data were used as independent derivation and validation sets to estimate the relationship between SF-12 scores and EQ-5D index scores, controlling for sociodemographic characteristics and comorbidity burden. Prediction equations for end-users who only have access to SF-12 scores were derived and compared. The empirical performance of censored least absolute deviations (CLAD), Tobit, and ordinary least squares (OLS) analytic methods were compared by calculating the mean prediction error in the validation set. Results . The fully specified CLAD model resulted in the lowest mean prediction error, followed by OLS and Tobit. The CLAD prediction equation based only on SF-12 scores performed better than the fully specified OLS and Tobit models. Conclusion . The current research provides an algorithm for mapping EQ-5D index scores from the SF-12. This algorithm may provide analysts with an avenue to obtain appropriate preference-based health-related quality-of-life scores for use in cost-effectiveness analyses when only SF-12 data are available.

Suggested Citation

  • Patrick W. Sullivan & Vahram Ghushchyan, 2006. "Mapping the EQ-5D Index from the SF-12: US General Population Preferences in a Nationally Representative Sample," Medical Decision Making, , vol. 26(4), pages 401-409, July.
    • Handle: RePEc:sae:medema:v:26:y:2006:i:4:p:401-409
    DOI: 10.1177/0272989X06290496
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    References listed on IDEAS

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    1. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
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    1. Billingsley Kaambwa & Lucinda Billingham & Stirling Bryan, 2013. "Mapping utility scores from the Barthel index," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(2), pages 231-241, April.
    2. Wang, Chao & Li, Qing & Sweetman, Arthur & Hurley, Jeremiah, 2015. "Mandatory universal drug plan, access to health care and health: Evidence from Canada," Journal of Health Economics, Elsevier, vol. 44(C), pages 80-96.
    3. Rowen, D & Brazier, J & Roberts, J, 2008. "Mapping SF-36 onto the EQ-5D index: how reliable is the relationship?," MPRA Paper 29831, University Library of Munich, Germany.
    4. Peiyi Lu & Ying Liang, 2016. "Health-Related Quality of Life of Young Chinese Civil Servants Working in Local Government: Comparison of SF-12 and EQ5D," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 11(4), pages 1445-1464, December.
    5. Mengjun Wu & John Brazier & Benjamin Kearns & Clare Relton & Christine Smith & Cindy Cooper, 2015. "Examining the impact of 11 long-standing health conditions on health-related quality of life using the EQ-5D in a general population sample," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(2), pages 141-151, March.
    6. Brazier, JE & Yang, Y & Tsuchiya, A, 2008. "A review of studies mapping (or cross walking) from non-preference based measures of health to generic preference-based measures," MPRA Paper 29808, University Library of Munich, Germany.
    7. Wijnen, Ben F.M. & Mosweu, Iris & Majoie, Marian H.J.M. & Ridsdale, Leone & de Kinderen, Reina J.A. & Evers, Silvia M.A.A. & McCrone, Paul, 2018. "A comparison of the responsiveness of EQ-5D-5L and the QOLIE-31P and mapping of QOLIE-31P to EQ-5D-5L in epilepsy," LSE Research Online Documents on Economics 106170, London School of Economics and Political Science, LSE Library.
    8. Ben F. M. Wijnen & Iris Mosweu & Marian H. J. M. Majoie & Leone Ridsdale & Reina J. A. Kinderen & Silvia M. A. A. Evers & Paul McCrone, 2018. "A comparison of the responsiveness of EQ-5D-5L and the QOLIE-31P and mapping of QOLIE-31P to EQ-5D-5L in epilepsy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(6), pages 861-870, July.
    9. John Brazier & Yaling Yang & Aki Tsuchiya & Donna Rowen, 2010. "A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(2), pages 215-225, April.
    10. Christine McDonough & Anna Tosteson, 2007. "Measuring Preferences for Cost-Utility Analysis," PharmacoEconomics, Springer, vol. 25(2), pages 93-106, February.
    11. Ann-Jean C C Beck & Jacobien M Kieffer & Valesca P Retèl & Lydia F J van Overveld & Robert P Takes & Michiel W M van den Brekel & Wim H van Harten & Martijn M Stuiver, 2019. "Mapping the EORTC QLQ-C30 and QLQ-H&N35 to the EQ-5D for head and neck cancer: Can disease-specific utilities be obtained?," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-16, December.
    12. Mona Aghdaee & Bonny Parkinson & Kompal Sinha & Yuanyuan Gu & Rajan Sharma & Emma Olin & Henry Cutler, 2022. "An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1525-1557, August.

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