Predicting the EQ-5D-3L Preference Index from the SF-12 Health Survey in a National US Sample

In this appendix we explain how to use our estimates and software to predict the EQ-5D when only the SF-12 is available. 1) Using our estimates: In the following example, we use estimates from the Mixture 2 model to make predictions based on classification (CEC). Table 2 shows parameters in the EQ-5D scale but in this example we use parameters in the reversed scale (1-EQ-5D) and we also use estimates with more decimal places than in Table 2. We calculate predicted values for an individual with PCS=48, MCS=30, and age= 50. Because we centered our covariates (see paper for details) these values correspond to-2,-20, and-15, respectively, in the centered scale. First, we calculate the expected value for each Tobit component. In the Tobit model, the estimated parameters correspond to the latent variable, which is assumed to distribute normal, while our interest is on the censored predictions. To obtain censored predictions, we first calculate predictions for the latent variables: The estimated standard deviations of component 1 and 2 are σ1 = .0547989 and σ2 = .1521847. The values µ1, µ2, σ1, σ2 enter into the calculation of the censored predictions as parameters of the cumulative standard normal distribution and the standard normal density (34). We recommend using Stata or another statistical package for this step: Because in this particular example predictions corresponding to PCS=48, MCS=30 and age= 50 were not censored, u1c = µ1 = 0.280568 and u2c = µ2 = 0.756231. Second, we calculate the probability of belonging to each class in the multinomial scale and then in the probability scale. In the multinomial scale: