Mapping QLQ-C30, HAQ, and MSIS-29 on EQ-5D

Background. Responses on condition-specific instruments can be mapped on the EQ-5D to estimate utility values for economic evaluation. Mapping functions differ in predictive quality, and not all condition-specific measures are suitable for estimating EQ-5D utilities. We mapped QLQC30, HAQ, and MSIS-29 on the EQ-5D and compared the quality of the mapping functions with statistical and clinical indicators. Methods. We used 4 data sets that included both the EQ-5D and a condition-specific measure to develop ordinary least squares regression equations. For the QLQ-C30, we used a multiple myeloma data set and a non-Hodgkin lymphoma one. An early arthritis cohort was used for the HAQ, and a cohort of patients with relapsing remitting or secondary progressive multiple sclerosis was used for the MSIS-29. We assessed the predictive quality of the mapping functions with the root mean square error (RMSE) and mean absolute error (MAE) and the ability to discriminate among relevant clinical subgroups. Pearson correlations between the condition-specific measures and items of the EQ-5D were used to determine if there is a relationship between the quality of the mapping functions and the amount of correlated content between the used measures. Results. The QLQ-C30 had the highest correlation with EQ-5D items. Average %RMSE was best for the QLQ-C30 with 10.9%, 12.2% for the HAQ, and 13.6% for the MSIS-29. The mappings predicted mean EQ-5D utilities without significant differences with observed utilities and discriminated between relevant clinical groups, except for the HAQ model. Conclusions. The preferred mapping functions in this study seem suitable for estimating EQ-5D utilities for economic evaluation. However, this research shows that lower correlations between instruments lead to less predictive quality. Using additional validation tests besides reporting statistical measures of error improves the assessment of predictive quality.

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