US Valuation of the EQ-5D Health States
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Abstract
PURPOSE: The EQ-5D is a brief, multiattribute, preference-based health status measure. This article describes the development of a statistical model for generating US population-based EQ-5D preference weights. METHODS: A multistage probability sample was selected from the US adult civilian noninstitutional population. Respondents valued 13 of 243 EQ-5D health states using the time trade-off (TTO) method. Data for 12 states were used in econometric modeling. The TTO valuations were linearly transformed to lie on the interval [-1, 1]. Methods were investigated to account for interaction effects caused by having problems in multiple EQ-5D dimensions. Several alternative model specifications (eg, pooled least squares, random effects) also were considered. A modified split-sample approach was used to evaluate the predictive accuracy of the models. All statistical analyses took into account the clustering and disproportionate selection probabilities inherent in our sampling design. RESULTS: Our D1 model for the EQ-5D included ordinal terms to capture the effect of departures from perfect health as well as interaction effects. A random effects specification of the D1 model yielded a good fit for the observed TTO data, with an overall R of 0.38, a mean absolute error of 0.025, and 7 prediction errors exceeding 0.05 in absolute magnitude. CONCLUSIONS: The D1 model best predicts the values for observed health states. The resulting preference weight estimates represent a significant enhancement of the EQ-5D's utility for health status assessment and economic analysis in the US.
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The record
- Venue
- Medical Care
- Topic
- Health Systems, Economic Evaluations, Quality of Life
- Field
- Economics, Econometrics and Finance
- Canadian institutions
- Institute of Health EconomicsUniversity of Alberta
- Funders
- Agency for Healthcare Research and Quality
- Keywords
- EQ-5DStatisticsEconometricsPreferenceMathematicsPopulationSample size determinationRandom effects modelSampling (signal processing)Sample (material)Simple random sampleValuation (finance)Ordinal dataComputer scienceEconomicsMedicineMeta-analysisEnvironmental health
- Has abstract in OpenAlex
- yes