A Value Set for the EQ-5D Based on Experienced Health States

Abstract

Decision makers responsible for allocation of healthcare resources may require that health states are valued by the population for whom they are making decisions. To achieve this, health-state descriptions can be combined with a value set that reflects the valuations of the target population. In the decision-utility approach, such a value set is at least partly based on wants and expectations regarding given health states. This may reflect aspects different from the health state experienced and valued by a respondent. To derive a value set that is completely based on experienced health states, emphasising the patient's perspective, and test its predictive performance in comparison with established approaches. Problem descriptions and rating scale valuations of the EQ-5D were drawn from two representative German population surveys in 2006 and 2007. Two models based on given health states but differing in valuation method (1a, b) were analysed, along with three models based on experienced health states: (2) ordinary least squares regression; (3) scale-transformed regression; and (4) a generalized linear model with binomial error distribution and constraint parameter estimation. The models were compared with respect to issues in specification, and accuracy in predicting the actual valuations of experienced health states in a new data set, using correlation, mean error and ranking measures for the latter. In addition, the impact of standardizing experience-based index models for age and sex of the subjects was investigated. Models 1 (a, b), 2 and 3 partly led to plausible and comparable parameter estimates, but also led to problems of insignificance and inconsistencies in some of the estimates. Model 4 achieved consistency and featured partly equivalent and partly better predictive accuracy. Using this model, mean valuations of health states were much better predicted by the experience-based approach than by the decision-utility approach, especially for health states that frequently (>10) occurred in the population sample. Standardizing the experience-based index models for age and sex further improved predictive accuracy and strengthened the position of model 4. A value set for the EQ-5D can be plausibly estimated from experience-based valuations. The approach offers an alternative to decision makers who prefer experience-based valuation over decision utilities in the measurement of health outcome. Although usefulness in population samples was shown, use in a clinical context will first require indication-specific tests. Current limitations include use in a general population only, and a restricted range of health states covered.