Valuing states from multiple measures on the same visual analogue sale: a feasibility study

Area of Science:

• Health Economics
• Outcomes Research
• Psychometrics

Background:

• Empirical mapping between health measures often relies on regression analyses of self-reported data.
• Existing methods assume overlap in descriptive systems and applicability across different populations, which can be limiting.

Purpose of the Study:

• To assess the feasibility of a new value-based mapping approach between preference-based measures (PBM).
• To utilize general population visual analogue scale (VAS) values as a common metric for cross-measure comparisons.

Main Methods:

• A valuation study involving 502 UK general population members.
• Simultaneous valuation of health states from six PBM (EQ-5D, SF-6D, HUI2, AQL-5D, OPUS, ICECAP) using ranking and VAS tasks.
• Regression analysis to link VAS values to existing 'tariff' values and estimate relationships between PBM.

Main Results:

• Demonstrated the feasibility of using general population VAS values for mapping between diverse PBM.
• Established a method to estimate relationships between six different preference-based measures.
• Developed a 'value-based mapping' technique for enhanced cross-measure comparability.

Conclusions:

• The proposed value-based mapping method offers a viable alternative to traditional empirical mapping techniques.
• This approach has significant potential for improving evidence synthesis and facilitating cross-programme comparisons in health research.
• Enables more robust comparisons when studies employ different preference-based measures.