OBJECTIVES: The purpose of this analysis was to develop a mapping algorithm to estimate EQ-5D utilities based on the 25-item National Eye Institute Visual Functioning Questionnaire (VFQ-25), a patient-reported outcome measure developed to evaluate vision-specific functioning. METHODS: The dataset comprised 951 paired EQ-5D/VFQ-25 observations from 344 patients in RESTORE, a 12-month, randomized, double-masked trial in patients with visual impairment due to diabetic macular edema. EQ-5D index scores were calculated based on the UK tariff. We evaluated 11 models and 4 separate predictor lists of VFQ-25 subscales to estimate utility as a function of VFQ-25 score, based on 4 models: Tobit, CLAD (Censored Least Absolute Deviation), GEE (Generalized Estimating Equation) and reverse two-part GEE models (which address the strong ceiling effect and left-skewed distribution of the EQ-5D). Model performance was assessed by ten-fold cross-validation comparing root mean squared error (RMSE), mean absolute error (MAE) and correlation with EQ-5D score (Spearman R-squared).   RESULTS: Mapping results were similar across all techniques and predictor lists. The reverse two-part GEE model had the best predictive performance (RMSE 0.199, MAE 0.140) and used fewest predictors, but correlated relatively weakly with the original EQ-5D results (Spearman R-squared 0.34).   CONCLUSIONS: Mapping VFQ-25 scores to EQ-5D utilities results in low predictive power independent of the modelling methodology applied. The difficulties in this mapping exercise are likely the result of the inability of the EQ-5D to discriminate vision-related activities.