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Using the Analytic Hierarchy Process to Derive Health State Utilities from Ordinal Preference Data
Brian P Reddy1, Roisin Adams1, Cathal Walsh2
1National Centre for Pharmacoeconomics, St. James Hospital, Dublin, Ireland.
Summary
The analytic hierarchy process offers a simpler way to generate health utilities from preference data, avoiding complex time trade-off methods and improving measurement of health states. This approach incorporates more preference data, enhancing economic evaluations in healthcare.
Area of Science:
- Health Economics
- Decision Analysis
- Psychometrics
Background:
- The EuroQol five-dimensional questionnaire is standard for health economic evaluations, using time trade-off (TTO) for preferences.
- Current TTO methods face challenges with 'worse-than-dead' states and are cognitively demanding.
- Extreme health state valuations are often rounded, excluding valuable preference data.
Purpose of the Study:
- To explore the analytic hierarchy process (AHP) for generating health utilities from ordinal health state relationships.
- To apply AHP to the UK Measurement and Valuation of Health (MVH) dataset for health state preferences.
- To incorporate previously excluded preference data into utility generation.
Main Methods:
- The analytic hierarchy process (AHP) was explained and applied.
- Five distinct structures for pairwise health state preference comparisons were detailed (concave, convex, linear).
- Optimization techniques were used to minimize errors between AHP-derived utilities and original TTO data.
Main Results:
- All AHP approaches effectively predicted health state rankings.
- Derived utilities exhibited an unconventional, bunched shape compared to TTO.
- An optimized AHP approach improved agreement with existing TTO utilities.
Conclusions:
- The AHP approach can convert ordinal preference data into cardinal utilities.
- This method is simpler and less cognitively demanding than TTO elicitation.
- AHP avoids the need to artificially adjust preference ranking results.
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