The original 3-level EQ-5D (EQ-5D-3L) includes 5 dimensions with 3 levels of problems per dimension. Since 2010, a more sensitive version with 5 levels of problems per dimension (EQ-5D-5L) has become available. Population value sets have been developed for both versions of the questionnaire. The objective of this research was to develop a mapping function to link EQ-5D-3L responses to value sets for the EQ-5D-5L.
Various algorithms were developed to link EQ-5D-3L and EQ-5D-5L responses using data from an observational study including members of 10 subgroups (N = 3580) who completed both versions of the questionnaire. Nonparametric and ordinal logistic regression models were fit to the data and compared using Akaike’s information criterion (AIC) as well as the mean absolute error and root mean squared error of predictions. Results were contrasted qualitatively and quantitatively with those of an alternative copula-based approach.
Including indicants of problems for other EQ-5D-3L dimensions as regressors in the modeling yielded the greatest improvement in prediction accuracy. Adding age and gender lowered the AIC without improving predictions, while including a latent factor lowered the AIC further and slightly improved predictive accuracy. Models that conditioned on problems in other EQ-5D-3L dimensions yielded more accurate predictions than the alternative copula-based approach in subgroups defined by age and gender.
We present novel algorithms to map EQ-5D-3L responses to EQ-5D-5L value sets. The recommended approach is based on an ordinal logistic regression that disregards age and gender and accounts for unobserved heterogeneity using a latent factor.
Ben A. van Hout James W. Shaw