Developing new mapping algorithms
SWEENEY, REGRESSION, BEST
The variables selected based on the estimation sample and the AIC criteria for the total, dimension and item-level equations are listed in the first column of Table Best mapping equations from PedsQL to CHU9D utility scoresVariables selected based on AIC criterion and models estimated using OLS. The estimation sample was used (The best estimation method for the three final equations was OLS, followed by CLAD, GLM, and BETA (Table Prediction summary of estimation sample—goodness-of-fit results from total, dimension, and item-level mappingsThe sample size for the item-level mapping is slightly smaller (648 vs. 674) due to missing values in some items*CHU9D predicted values were truncated at 1The twelve models with the AIC-based specifications were applied to the validation sample and goodness of fit further assessed (Table Prediction summary of validation sample—goodness-of-fit results from total, dimension, and item-level mappingsThe sample size for the item-level mapping is slightly smaller (161 vs. 168) due to missing values in some itemsThe parameter estimates of the final mapping equations based on the CYPHP sample are presented in Table The CYPHP mappings for dimension and total scores are positively related to the Sweeney mappings (Table S4). The results of the regression analysis confirm our previous results that, on average, the predictive scores using the CYPHP models are higher than those estimated using the Sweeney models. The exclusion of demographic characteristics (age in this case) resulted in little to no change to the model performance (Table S5).