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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

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Mapping between 6 Multiattribute Utility Instruments.

Gang Chen¹, Munir A Khan², Angelo Iezzi²

¹Flinders Health Economics Group, Flinders University, Adelaide, Australia (GC, JR)

Medical Decision Making : an International Journal of the Society for Medical Decision Making

|April 5, 2015

Summary

This study developed mapping functions to standardize multiattribute utility (MAU) instruments, improving health economic evaluation comparability. These crosswalks align measurement scales, enhancing confidence in quality-adjusted life year calculations across diverse studies.

Keywords:

cost-effectiveness analysis cost-utility analysis health-related quality of life mapping utility

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Area of Science:

Health economics
Outcomes research
Psychometrics

Background:

Cost-utility analyses rely on multiattribute utility (MAU) instruments for health state utilities.
Discrepancies between MAU instruments hinder the comparison of economic evaluation studies.
Standardization is crucial for reliable quality-adjusted life year (QALY) calculations.

Purpose of the Study:

To develop and present 30 "crosswalk" mapping functions between six common MAU instruments.
To enable direct comparison of health state utilities estimated by different instruments.
To improve the consistency and comparability of cost-utility analyses.

Main Methods:

A multi-instrument comparison survey was conducted with 8022 respondents across 6 countries.
Six MAU instruments (EQ-5D-5L, SF-6D, HUI 3, 15D, QWB, AQoL-8D) were administered.
Mapping functions were estimated using ordinary least squares, generalized linear model, censored least absolute deviations, and MM-estimator techniques.

Main Results:

Goodness-of-fit indicators were comparable to published studies.
Developed transformations significantly reduced discrepancies between predicted health state utilities.
Incremental utilities aligned closely at sample means, indicating scale harmonization.

Conclusions:

The presented mapping functions effectively align the measurement scales of different MAU instruments.
Utilizing these crosswalks enhances confidence in comparing economic evaluation studies using various MAU tools.
This standardization facilitates more reliable QALY estimations and health policy decisions.