Mapping the EQ-5D index from the SF-12: US general population preferences in a nationally representative sample

Abstract

Background: Previous mapping algorithms estimating EQ-5D index scores from the SF-12 were based on preferences from a UK community sample. However, preferences based on the general US population are most appropriate for costeffectiveness analyses done from the societal perspective in the United States.

Objective: To provide a mapping algorithm for estimating EQ-5D index scores from the SF-12 based on a nationally representative sample and using preferences based on the general US population:

Methods: The Medical Expenditure Panel Survey (MEPS) 2002 and 2000 data were used as independent derivation and validation sets to estimate the relationship between SF-12 scores and EQ-5D index scores, controlling for sociodemographic characteristics and comorbidity burden. Prediction equations for end-users who only have access to SF-12 scores were derived and compared. The empirical performance of censored least absolute deviations (CLAD), Tobit, and ordinary least squares (OLS) analytic methods were compared by calculating the mean prediction error in the validation set.

Results: The fully specified CLAD model resulted in the lowest mean prediction error, followed by OLS and Tobit. The CLAD prediction equation based only on SF-12 scores performed better than the fully specified OLS and Tobit models.

Conclusion: The current research provides an algorithm for mapping EQ-5D index scores from the SF-12. This algorithm may provide analysts with an avenue to obtain appropriate preference-based health-related quality-of-life scores for use in cost-effectiveness analyses when only SF-12 data are available.

Figure 1

Actual, ordinary least squares (OLS), censored least absolute deviations (CLAD), and Tobit predicted EQ-5D index scores in the 2000 and 2002 Medical Expenditure Panel Survey (MEPS) sample.