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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

  • Clara MukuriaEmail author
  • Donna Rowen
  • Sue Harnan
  • Andrew Rawdin
  • Ruth Wong
  • Roberta Ara
  • John Brazier
Systematic Review

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.

Notes

Author Contributions

CM and DR reviewed studies, extracted and analysed the data from the review, and wrote the manuscript. SH and AR reviewed studies, extracted data and contributed to the manuscript. RW undertook the searches and contributed to the manuscript. RA and JB were involved in designing the analysis and contributed to the manuscript.

Compliance with Ethical Standards

Conflict of interest

JB and CM were supported in the preparation and submission of this paper by the HEOM Theme of the NIHR CLAHRC Yorkshire and Humber: www.clahrc-yh.nihr.ac.uk. The views expressed are those of the author(s), and not necessarily those of the NHS, the NIHR or the Department of Health. CM, DR, SH, AR, RW, RA and JB have no conflicts of interests to declare.

Data availability

Full data extracted from each of the studies is available in the electronic supplements.

Supplementary material

40258_2019_467_MOESM1_ESM.docx (30 kb)
Supplementary material 1 (DOCX 30 kb)
40258_2019_467_MOESM2_ESM.docx (52 kb)
Supplementary material 2 (DOCX 51 kb)
40258_2019_467_MOESM3_ESM.xlsx (80 kb)
Supplementary material 3 (XLSX 79 kb)

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Health and Related Research (ScHARR)University of SheffieldSheffieldUK

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