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: In total, 3866 observations for 1719 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 10 tumor-type specific samples as well as using differences between estimated quality-adjusted life years. Thirteen new algorithms were estimated using a subset of the Cancer 2015 data (3203 observations for 1419 patients) applying various linear, response mapping, beta, and mixture models. Validation was performed using 2 data sets composed of patients with varying disease severity not used in the estimation and all available algorithms ranked on their performance.
Results: None of the 5 existing algorithms offer an improvement in predictive accuracy over preferred algorithms from previous validation studies. Of the newly estimated algorithms, a 2-part beta model performed the best across the validation criteria and in data sets composed of patients with different levels of disease severity. Validation results did, however, vary widely between the 2 data sets, 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.
Keywords: cancer; condition-specific non-preference-based measures; external validation; generic preference-based measures; quality of life; regression models.