Mapping the EORTC-QLQ-C30 to the EQ-5D-3L: An assessment of existing and newly developed algorithms

Objectives: To assess the external validity of mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses not previously validated and to assess whether statistical models not previously applied are better suited for mapping the EORTC QLQ-C30 to the EQ-5D-3L. Methods: 3,866 observations for 1,719 patients from a longitudinal study (Cancer 2015) were used to validate existing algorithms. Predictive accuracy was compared to previously validated algorithms using root mean squared error, mean absolute error across the EQ-5D-3L range and for ten tumour-type specific samples as well as using differences between estimated QALYs. Thirteen new algorithms were estimated using a subset of the Cancer 2015 data (3,203 observations for 1,419 patients) applying various linear, response mapping, beta and mixture models. Validation was performed using two datasets composed of patients with varying disease severity not used in the estimation and all available algorithms ranked on their performance. Results: None of the five existing algorithms offer an improvement in predictive accuracy over preferred algorithms from previous validation studies. Of the newly estimated algorithms, a two-part beta model performed the best across the validation criteria and in datasets composed of patients with different levels of disease severity. Validation results did, however, vary widely between the two datasets and the most accurate algorithm appears to depend on health state severity as the distribution of observed EQ-5D-3L values varies. Linear models performed better for patients in relatively good health, whereas beta, mixture and response mapping models performed better for patients in worse health. Conclusion: The most appropriate mapping algorithm to apply in practice may depend on the disease severity of the patient sample whose utility values are being predicted