Mapping the EQ-5D index by UPDRS and PDQ-8 in patients with Parkinson’s disease

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Mapping the EQ-5D index by UPDRS and PDQ-8 in patients with Parkinson’s disease

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Title: Mapping the EQ-5D index by UPDRS and PDQ-8 in patients with Parkinson’s disease
Author: Dams, Judith; Klotsche, Jens; Bornschein, Bernhard; Reese, Jens P; Balzer-Geldsetzer, Monika; Winter, Yaroslav; Schrag, Anette; Siderowf, Andrew; Oertel, Wolfgang H; Deuschl, Günther; Siebert, Uwe; Dodel, Richard

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Citation: Dams, Judith, Jens Klotsche, Bernhard Bornschein, Jens P Reese, Monika Balzer-Geldsetzer, Yaroslav Winter, Anette Schrag, et al. 2013. Mapping the eq-5d index by updrs and pdq-8 in patients with parkinson’s disease. Health and Quality of Life Outcomes 11: 35.
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Abstract: Background: Clinical studies employ the Unified Parkinson’s Disease Rating Scale (UPDRS) to measure the severity of Parkinson’s disease. Evaluations often fail to consider the health-related quality of life (HrQoL) or apply disease-specific instruments. Health-economic studies normally use estimates of utilities to calculate quality-adjusted life years. We aimed to develop an estimation algorithm for EuroQol- 5 dimensions (EQ-5D)-based utilities from the clinical UPDRS or disease-specific HrQoL data in the absence of original utilities estimates. Methods: Linear and fractional polynomial regression analyses were performed with data from a study of Parkinson’s disease patients (n=138) to predict the EQ-5D index values from UPDRS and Parkinson’s disease questionnaire eight dimensions (PDQ-8) data. German and European weights were used to calculate the EQ-5D index. The models were compared by R2, the root mean square error (RMS), the Bayesian information criterion, and Pregibon’s link test. Three independent data sets validated the models. Results: The regression analyses resulted in a single best prediction model (R2: 0.713 and 0.684, RMS: 0.139 and 13.78 for indices with German and European weights, respectively) consisting of UPDRS subscores II, III, IVa-c as predictors. When the PDQ-8 items were utilised as independent variables, the model resulted in an R2 of 0.60 and 0.67. The independent data confirmed the prediction models. Conclusion: The best results were obtained from a model consisting of UPDRS subscores II, III, IVa-c. Although a good model fit was observed, primary EQ-5D data are always preferable. Further validation of the prediction algorithm within large, independent studies is necessary prior to its generalised use.
Published Version: doi:10.1186/1477-7525-11-35
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