OBJECTIVES: To develop a quantitative algorithm that transforms the Unified Parkinson's Disease Rating Scale (UPDRS), which is the most frequently used instrument to evaluate different clinical dimensions of Parkinson's Disease (PD), into EuroQoL (EQ-5D) values. METHODS: A total of 157 PD patients (mean age: 67 yrs., 63% male, mean total UPDRS: 44, mean EQ-5D: 0.74) were recruited in a prospective study at a German movement disorders center. Both EQ-5D and UPDRS were evaluated at baseline in 124 patients. Spearman correlation coefficient (R) was used to test whether total UPDRS score, sub scores (U2,U3,U4), and other patient characteristics were univariately associated with EQ-5D. A transformation algorithm with UPDRS sub scores as predictors and EQ-5D as outcome was derived using multivariate regression analysis. Goodness-of-fit was determined by adjusted R-square and the Hosmer-Lemeshow method. RESULTS: In the univariate analysis, all UDPRS sub scores were significantly (p <0.05) correlated with clinical stage on the Hoehn & Yahr (HY) scale. Significant inverse correlation (all p <0.001) was found between EQ-5D and total UPDRS (R = -0.67), U2 (R = -0.63), U3 (R = -0.60), U4 (R = -0.59), and HY stage (R = -0.52). Multivariate analysis showed that 52% of the variance in EQ-5D could be explained by the following equation: EQ-5D = (99.62 - 1.36xU2 - 0.13xU3 - 1.66xU4)/100. The Hosmer-Lemeshow test showed good predictive power. Using different mathematical functions (e.g., log, logit, square) of predictors, utilities or disutilities, and inclusion of interaction terms did not substantially increase adjusted R-square. CONCLUSIONS: We suggest a simple, parsimonious, and easily feasible algorithm for the transformation of UPDRS scores into EQ-5D-based utilities. The purpose of this function is not to predict individual quality of life, but mean utilities for populations with a specific UPDRS configuration, which may be used in the evaluation of intervention's overall effectiveness or cost-effectiveness. This algorithm can be applied to existing UPDRS data sets and used in cost-utility analyses of health technologies.