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Mapping between 6 Multiattribute Utility Instruments

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  • Gang Chen
  • Munir A. Khan
  • Angelo Iezzi
  • Julie Ratcliffe
  • Jeff Richardson

Abstract

Background: Cost-utility analyses commonly employ a multiattribute utility (MAU) instrument to estimate the health state utilities, which are needed to calculate quality-adjusted life years. Different MAU instruments predict significantly different utilities, which makes comparison of results from different evaluation studies problematical. Aim: This article presents mapping functions (“crosswalks†) from 6 MAU instruments (EQ-5D-5L, SF-6D, Health Utilities Index 3 [HUI 3], 15D, Quality of Well-Being [QWB], and Assessment of Quality of Life 8D [AQoL-8D]) to each of the other 5 instruments in the study: a total of 30 mapping functions. Methods: Data were obtained from a multi-instrument comparison survey of the public and patients in 7 disease areas conducted in 6 countries (Australia, Canada, Germany, Norway, United Kingdom, and United States). The 8022 respondents were administered each of the 6 study instruments. Mapping equations between each instrument pair were estimated using 4 econometric techniques: ordinary least squares, generalized linear model, censored least absolute deviations, and, for the first time, a robust MM-estimator. Results: Goodness-of-fit indicators for each of the results are within the range of published studies. Transformations reduced discrepancies between predicted utilities. Incremental utilities, which determine the value of quality-related health benefits, are almost perfectly aligned at the sample means. Conclusion: Transformations presented here align the measurement scales of MAU instruments. Their use will increase confidence in the comparability of evaluation studies, which have employed different MAU instruments.

Suggested Citation

  • Gang Chen & Munir A. Khan & Angelo Iezzi & Julie Ratcliffe & Jeff Richardson, 2016. "Mapping between 6 Multiattribute Utility Instruments," Medical Decision Making, , vol. 36(2), pages 160-175, February.
    • Handle: RePEc:sae:medema:v:36:y:2016:i:2:p:160-175
    DOI: 10.1177/0272989X15578127
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    References listed on IDEAS

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    1. Janelle Seymour & Paul McNamee & Anthony Scott & Michela Tinelli, 2010. "Shedding new light onto the ceiling and floor? A quantile regression approach to compare EQ‐5D and SF‐6D responses," Health Economics, John Wiley & Sons, Ltd., vol. 19(6), pages 683-696, June.
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    4. Duncan Mortimer & Leonie Segal, 2008. "Comparing the Incomparable? A Systematic Review of Competing Techniques for Converting Descriptive Measures of Health Status into QALY-Weights," Medical Decision Making, , vol. 28(1), pages 66-89, January.
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    Cited by:

    1. Lei Si & Liudan Tu & Ya Xie & Gang Chen & Mickaël Hiligsmann & Mingcan Yang & Yanli Zhang & Xi Zhang & Yutong Jiang & Qiujing Wei & Jieruo Gu & Andrew J. Palmer, 2022. "Evaluating Health Related Quality of Life in Older People at Risk of Osteoporotic Fracture: A Head-to-Head Comparison of the EQ-5D-5L and AQoL-6D," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 160(2), pages 809-824, April.
    2. Shitong Xie & Jing Wu & Gang Chen, 2024. "Comparative performance and mapping algorithms between EQ-5D-5L and SF-6Dv2 among the Chinese general population," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(1), pages 7-19, February.
    3. Lan Gao & Wei Luo & Utsana Tonmukayakul & Marj Moodie & Gang Chen, 2021. "Mapping MacNew Heart Disease Quality of Life Questionnaire onto country-specific EQ-5D-5L utility scores: a comparison of traditional regression models with a machine learning technique," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(2), pages 341-350, March.

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