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Impact of mapped EQ-5D utilities on cost-effectiveness analysis: in the case of dialysis treatments

Author

Listed:
  • Fan Yang

    (University of York)

  • Nancy Devlin

    (Office of Health Economics)

  • Nan Luo

    (National University of Singapore)

Abstract

Objectives This study aimed to evaluate the performance of EQ-5D data mapped from SF-12 in terms of estimating cost effectiveness in cost-utility analysis (CUA). The comparability of SF-6D (derived from SF-12) was also assessed. Methods Incremental quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) were calculated based on two Markov models assessing the cost effectiveness of haemodialysis (HD) and peritoneal dialysis (PD) using utility values based on EQ-5D-5L, EQ-5D using three direct-mapping algorithms and two response-mapping algorithms (mEQ-5D), and SF-6D. Bootstrap method was used to estimate the 95% confidence interval (percentile method) of incremental QALYs and ICERs with 1000 replications for the utilities. Results In both models, compared to the observed EQ-5D values, mEQ-5D values expressed much lower incremental QALYs (range − 14.9 to − 33.2%) and much higher ICERs (range 17.5 to 49.7%). SF-6D also estimated lower incremental QALYs (− 29.0 and − 14.9%) and higher ICERs (40.9 and 17.5%) than did the observed EQ-5D. The 95% confidence interval of incremental QALYs and ICERs confirmed the lower incremental QALYs and higher ICERs estimated using mEQ-5D and SF-6D. Conclusion Compared to observed EQ-5D, EQ-5D mapped from SF-12 and SF-6D would under-estimate the QALYs gained in cost-utility analysis and thus lead to higher ICERs. It would be more sensible to conduct CUA studies using directly collected EQ-5D data and to designate one single preference-based measure as reference case in a jurisdiction to achieve consistency in healthcare decision-making.

Suggested Citation

  • Fan Yang & Nancy Devlin & Nan Luo, 2019. "Impact of mapped EQ-5D utilities on cost-effectiveness analysis: in the case of dialysis treatments," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(1), pages 99-105, February.
    • Handle: RePEc:spr:eujhec:v:20:y:2019:i:1:d:10.1007_s10198-018-0987-x
    DOI: 10.1007/s10198-018-0987-x
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    References listed on IDEAS

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    Cited by:

    1. 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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    More about this item

    Keywords

    Cost-effectiveness; Dialysis; EQ-5D; Mapping; SF-6D;
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    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General

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