A Median Model for Predicting United States Population-Based EQ-5D Health State Preferences

Objective The D1 model that was developed to predict US societal preferences for EQ-5D health states addressed several important conceptual and statistical issues. However, it has been criticized for being too complex, failing to account for the nonnormal distribution of health state values, and the transformation of preferences for worse-than-death health states before estimation. This research was conducted to develop an improved model for predicting median preferences for EQ-5D health states for the US population. Methods Probability-weighted least absolute deviations regression was used to fit models to the time trade-off data collected in the US Valuation of the EQ-5D Health States study. No transformation was applied to the values for states considered worse than death. Several model specifications that differed with respect to explanatory variables were evaluated using two-sample cross-validation. Results The best-fitting model included only fixed effects for moderate or severe problems in each of the 5 EQ-5D dimensions and excluded a constant. This specification yielded rank correlations between observed and predicted values and median observed and predicted values of 0.635 and 0.991, respectively, as well as a median absolute error of 0.026. The predicted median preferences ranged from 1.00 for full health, to –0.81 for the worst possible health state. Conclusions Due to its simplicity and robustness, a median model is superior to other models for predicting US population preferences for EQ-5D health states. The predictions of this model are suggested for use in applications that require US societal health state values.