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An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

Author

Listed:
  • Clara Mukuria

    (University of Sheffield)

  • Donna Rowen

    (University of Sheffield)

  • Sue Harnan

    (University of Sheffield)

  • Andrew Rawdin

    (University of Sheffield)

  • Ruth Wong

    (University of Sheffield)

  • Roberta Ara

    (University of Sheffield)

  • John Brazier

    (University of Sheffield)

Abstract

Background Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values (HSUVs) from data obtained from another health-related quality of life (HRQoL) measure. There have been several methodological developments in this area since a previous review up to 2007. Objective To provide an updated review of all mapping studies that map from HRQoL measures to target generic preference-based measures (EQ-5D measures, SF-6D, HUI measures, QWB, AQoL measures, 15D/16D/17D, CHU-9D) published from January 2007 to October 2018. Data sources A systematic review of English language articles using a variety of approaches: searching electronic and utilities databases, citation searching, targeted journal and website searches. Study selection Full papers of studies that mapped from one health measure to a target preference-based measure using formal statistical regression techniques. Data extraction Undertaken by four authors using predefined data fields including measures, data used, econometric models and assessment of predictive ability. Results There were 180 papers with 233 mapping functions in total. Mapping functions were generated to obtain EQ-5D-3L/EQ-5D-5L-EQ-5D-Y (n = 147), SF-6D (n = 45), AQoL-4D/AQoL-8D (n = 12), HUI2/HUI3 (n = 13), 15D (n = 8) CHU-9D (n = 4) and QWB-SA (n = 4) HSUVs. A large number of different regression methods were used with ordinary least squares (OLS) still being the most common approach (used ≥ 75% times within each preference-based measure). The majority of studies assessed the predictive ability of the mapping functions using mean absolute or root mean squared errors (n = 192, 82%), but this was lower when considering errors across different categories of severity (n = 92, 39%) and plots of predictions (n = 120, 52%). Conclusions The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions.

Suggested Citation

  • Clara Mukuria & Donna Rowen & Sue Harnan & Andrew Rawdin & Ruth Wong & Roberta Ara & John Brazier, 2019. "An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values," Applied Health Economics and Health Policy, Springer, vol. 17(3), pages 295-313, June.
  • Handle: RePEc:spr:aphecp:v:17:y:2019:i:3:d:10.1007_s40258-019-00467-6
    DOI: 10.1007/s40258-019-00467-6
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    References listed on IDEAS

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    1. Montse Roset & Xavier Badia & Anna Forsythe & Susan Webb, 2013. "Mapping CushingQoL Scores onto SF-6D Utility Values in Patients with Cushing’s Syndrome," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 6(2), pages 103-111, June.
    2. Gang Chen & Julie Ratcliffe, 2015. "A Review of the Development and Application of Generic Multi-Attribute Utility Instruments for Paediatric Populations," PharmacoEconomics, Springer, vol. 33(10), pages 1013-1028, October.
    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. Caterina Conigliani & Andrea Manca & Andrea Tancredi, 2015. "Prediction of patient-reported outcome measures via multivariate ordered probit models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 567-591, June.
    5. Chun Fan Lee & Raymond Ng & Nan Luo & Yin Bun Cheung, 2018. "Development of Conversion Functions Mapping the FACT-B Total Score to the EQ-5D-5L Utility Value by Three Linking Methods and Comparison with the Ordinary Least Square Method," Applied Health Economics and Health Policy, Springer, vol. 16(5), pages 685-695, October.
    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, March.
    7. Tomas Mlcoch & Jan Tuzil & Liliana Sedova & Jiri Stolfa & Monika Urbanova & David Suchy & Andrea Smrzova & Jitka Jircikova & Tereza Hrnciarova & Karel Pavelka & Tomas Dolezal, 2018. "Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 11(3), pages 329-340, June.
    8. Monica Hernandez Alava & Allan Wailoo, 2015. "Fitting adjusted limited dependent variable mixture models to EQ-5D," Stata Journal, StataCorp LP, vol. 15(3), pages 737-750, September.
    9. Julie Ratcliffe & Elisabeth Huynh & Katherine Stevens & John Brazier & Michael Sawyer & Terry Flynn, 2016. "Nothing About Us Without Us? A Comparison of Adolescent and Adult Health‐State Values for the Child Health Utility‐9D Using Profile Case Best–Worst Scaling," Health Economics, John Wiley & Sons, Ltd., vol. 25(4), pages 486-496, April.
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    1. Rachel Houten’s journal round-up for 8th July 2019
      by Rachel Houten in The Academic Health Economists' Blog on 2019-07-08 11:00:07

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    2. Nicholas Mitsakakis & Karen E. Bremner & George Tomlinson & Murray Krahn, 2020. "Exploring the Benefits of Transformations in Health Utility Mapping," Medical Decision Making, , vol. 40(2), pages 183-197, February.

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