Mapping the Patient-Reported Outcomes Measurement Information System (PROMIS-29) to EQ-5D-5L

The Patient-Reported Outcomes Measurement Information System (PROMIS-29) is gaining popularity as healthcare system funders increasingly seek value-based care. However, it is limited in its ability to estimate utilities and thus inform economic evaluations. This study develops the first mapping algorithm for estimating EuroQol 5-Dimension 5-Level (EQ-5D-5L) utilities from PROMIS-29 responses using a large dataset and through extensive comparisons between econometric models.

An online survey was conducted to collect responses to PROMIS-29 and EQ-5D-5L from the general Australian population (N = 3013). Direct and indirect mapping methods were explored, including linear regression, Tobit, generalised linear model, censored regression model, beta regression (Betamix), the adjusted limited dependent variable mixture model (ALDVMM) and generalised ordered logit. The most robust model was selected by assessing the performance based on average ten-fold cross-validation geometric mean absolute error and geometric mean squared error, the predicted mean, maximum and minimum utilities, as well as the fitting across the entire distribution.

The direct approach using ALDVMM was considered the preferred model based on lowest geometric mean absolute error and geometric mean squared error in cross-validation (0.0882, 0.0299) and its superiority in predicting the actual observed mean, full health states and lower utility extremes. The robustness and precision in prediction across the entire distribution of utilities with ALDVMM suggest it is an accurate and valid mapping algorithm. Moreover, the suggested mapping algorithm outperformed previously published algorithms using Australian data, indicating the validity of this model for economic evaluations.

This study developed a robust algorithm to estimate EQ-5D-5L utilities from PROMIS-29. Consistent with the recent literature, the ALDVMM outperformed all other econometric models considered in this study, suggesting that the mixture models have relatively better performance and are an ideal candidate model for mapping.