Background: Mapping Patient-Reported Outcomes Measurement Information System-Global Health (PROMIS-GH) to EuroQol 5-dimension, three-level version (EQ-5D-3L) provides a utility score for use in quality-of-life and cost-effectiveness analyses. In 2009, Revicki et al. mapped the PROMIS-GH items to EQ-5D-3L utilities using linear regression (REVReg). More recently, regression was shown to be ill-suited for mapping to preference-based measures due to regression to the mean. Linear and equipercentile equating are alternative mapping methods that avoid the issue of regression to the mean. Another limitation of the prior models is that ordinal predictors were treated as continuous.
Methods: Using data collected from the PROMIS Wave 1 sample, we refit REVReg, treating the PROMIS-GH items as categorical variables (CATReg). We applied linear and equipercentile equating to the REVReg model (REVLE, REVequip) and the CATReg model (CATLE, CATequip). We validated and compared the predictive accuracy of these models in a large sample of neurological patients at a single tertiary-care hospital.
Results: In the neurological disease patient sample, CATLE produced the strongest correlations between estimated and observed EQ-5D-3L scores and had the lowest mean squared error. The CATequip model had the lowest mean absolute error and had estimated scores that best matched the overall distribution of observed scores.
Conclusions: Using linear and equipercentile equating, we created new models mapping PROMIS-GH items to EQ-5D-3L utility scores. EQ-5D-3L utility scores can be more accurately estimated using our models for use in cost-effectiveness studies or studies examining overall health-related quality of life.