Deriving a mapping algorithm for converting SF-36 scores to EQ-5D utility score in a Korean population

Background: There is no research on mapping algorithms between EQ-5D and SF-36 in Korea. The aim of this study was to derive a predictive model for converting the SF-36 health profile to the EQ-5D index using data from several studies. Methods: Individual data (n = 2211) were collected from three different studies and separated into derivation (n = 1660) and internal validation sets (n = 551). Data from 123 colon cancer patients were analyzed for external validation. The prediction models were analyzed using ordinary least-square (OLS) regression, two-part modeling, and multinomial logistic modeling using eight scale scores; two summary scores and the interaction terms of SF-36 were used as independent variables. The EQ-5D index using the Korean value set and each dimension of the EQ-5D were used as dependent variables. The mean absolute errors (MAE) and R-2 values of the internal and external validation dataset were used to evaluate model performance. Results: Our findings show that the three different scoring algorithms demonstrate similar performances in terms of MAE and R-2. After considering familiarity and parsimony, the OLS model (including Physical Function, Bodily Pain, Social Function, Role Emotional, and Mental Health) was found to be optimal as the final algorithm for use in this study. The MAEs of the OLS models demonstrated consistent results in both the derivation (0.087-0.109) and external validation sets (0.082-0.097). Conclusion: This study provides mapping algorithms for estimating the EQ-5D index from the SF-36 profile using individual data and confirms that these algorithms demonstrate high explanatory power and low prediction errors.