4.4 Article

Mapping PedsQL™ Generic Core Scales to EQ-5D-3L utility scores in transfusion-dependent thalassemia patients

Journal

EUROPEAN JOURNAL OF HEALTH ECONOMICS
Volume 22, Issue 5, Pages 735-747

Publisher

SPRINGER
DOI: 10.1007/s10198-021-01287-z

Keywords

Mapping algorithm; PedsQL; EQ-5D-3L; Utility scores; Quality of life; Thalassemia

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A mapping algorithm was developed to generate EQ-5D-3L utility scores from PedsQL GCS in patients with TDT, with GLM identified as the best performing estimation method. The algorithm could provide economic evaluation information for TDT patients when EQ-5D data is lacking, but caution should be taken when using it in patients with poor quality of life.
Purpose To develop a mapping algorithm for generating EQ-5D-3L utility scores from the PedsQL Generic Core Scales (PedsQL GCS) in patients with transfusion-dependent thalassemia (TDT). Methods The algorithm was developed using data from 345 TDT patients. Spearman's rank correlation was used to evaluate the conceptual overlap between the instruments. Model specifications were chosen using a stepwise regression. Both direct and response mapping methods were attempted. Six mapping estimation methods ordinary least squares (OLS), a log-transformed response using OLS, generalized linear model (GLM), two-part model (TPM), Tobit and multinomial logistic regression (MLOGIT) were tested to determine the root mean squared error (RMSE) and mean absolute error (MAE). Other criterion used were accuracy of the predicted utility score, proportions of absolute differences that was less than 0.03 and intraclass correlation coefficient. An in-sample, leave-one-out cross validation was conducted to test the generalizability of each model. Results The best performing model was specified with three out of the four PedsQL GCS scales-the physical, emotional and social functioning score. The best performing estimation method for direct mapping was a GLM with a RMSE of 0.1273 and MAE of 0.1016, while the best estimation method for response mapping was the MLOGIT with a RMSE of 0.1597 and MAE of 0.0826. Conclusion The mapping algorithm developed using the GLM would facilitate the calculation of utility scores to inform economic evaluations for TDT patients when EQ-5D data is not available. However, caution should be exercised when using this algorithm in patients who have poor quality of life.

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