Abstract

Objectives

This study aimed to generate mapping algorithms from the Patient-Reported Outcomes Measurement Information System Pediatric-25 Profile (PROMIS-25) to both EQ-5D-Y-3L responses (indirect mapping) and EQ-5D-Y-3L utilities (direct mapping).

Methods

A subset of data from the Australian Paediatric Multi-Instrument Comparison study data set was used, including participants aged 5 to 18 years who completed both the EQ-5D-Y-3L and PROMIS-25 (n = 1830). Both direct and indirect mapping approaches were used, exploring a range of regression models and predictor variables for each approach. For the direct mapping approach, the EQ-5D-Y-3L Australian value set was used, and sensitivity analyses were conducted using the EQ-5D-Y-3L Dutch value set. Five-fold internal cross-validation was used to select the optimal mapping models based on goodness-of-fit indicators, including the root mean square error (RMSE), mean absolute error (MAE), and concordance correlation coefficient. The final mapping algorithms reported are based on the full sample.

Results

The generalized ordered logit model using the PROMIS-25 raw domain scores as predictors was selected for predicting EQ-5D-Y-3L responses in the indirect mapping (RMSE, 0.1098; MAE, 0.0724). The Tobit model, also using the PROMIS-25 raw item scores as predictors, was the optimal direct mapping model for predicting Australian EQ-5D-Y-3L utilities (RMSE, 0.0994; MAE, 0.0712). The same models performed similarly well in sensitivity analyses using Dutch utilities.

Conclusions

The mapping algorithms provide a pathway for PROMIS-25 data to be converted directly to either an Australian or Dutch EQ-5D-Y-3L utility or EQ-5D-Y-3L responses where local value sets can be applied. This broadens the usability of PROMIS-25, enabling calculation of utilities for use in economic evaluation.