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.
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.
Authors: Corrine I Voils; Erica Levine; Jennifer M Gierisch; Jane Pendergast; Sarah L Hale; Megan A McVay; Shelby D Reed; William S Yancy; Gary Bennett; Elizabeth M Strawbridge; Allison C White; Ryan J Shaw Journal: Contemp Clin Trials Date: 2017-12-28 Impact factor: 2.226
Authors: Shelby D Reed; Yanhong Li; Helen A Dakin; Frauke Becker; Jose Leal; Stephanie M Gustavson; Bernt Kartman; Eric Wittbrodt; Robert J Mentz; Neha J Pagidipati; M Angelyn Bethel; Alastair M Gray; Rury R Holman; Adrian F Hernandez Journal: Diabetes Care Date: 2019-12-05 Impact factor: 19.112
Authors: Frauke Becker; Helen A Dakin; Shelby D Reed; Yanhong Li; José Leal; Stephanie M Gustavson; Eric Wittbrodt; Adrian F Hernandez; Alastair M Gray; Rury R Holman Journal: Diabetes Res Clin Pract Date: 2021-11-20 Impact factor: 5.602