OBJECTIVES: The EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS®) preference score (PROPr) are commonly-used preference-based measures. This study compares mapping and linking approaches to align the PROPr and the PROMIS domains included in the PROPr plus Anxiety with EQ-5D-5L item responses and preference score in US.

METHODS: A sample of 983 adults completed the online survey. Regression-based mapping methods and item response theory (IRT)-based linking methods were employed to align the scores based on a general population sample. Logistic regression mapped EQ-5D-5L item responses onto PROMIS domain scores, and ordinary least-squares regression mapped EQ-5D-5L preference scores onto PROMIS domains and PROPr preference scores. An IRT-based linking approach was used to generate bidirectional crosswalks between EQ-5D-5L item responses and relevant PROMIS domain scores.

RESULTS: The mean age of respondents was 50 years, with 54% female, 16% Hispanic and 77% White. EQ-5D-5L item responses were significantly accounted for by PROMIS domains of anxiety, depression, fatigue, pain interference, physical function, social roles, and sleep disturbance. EQ-5D-5L preference scores were accounted for by the same PROMIS domains, excluding anxiety and fatigue, and by the PROPr preference scores. Crosswalks were generated between EQ-5D-5L item responses and PROMIS domains of physical function, pain, and depression. Small differences (<11%) and moderate to high agreement (kappa>0.7) were found between most of predicted and observed scores across all three methods. A comparison between the mapping and linking approaches shows the mapping strategy exhibited better precision in estimating scores (discrepancy 8% vs 10%), while the linking method demonstrated a broader range of score prediction (1.55 vs 0.73).

CONCLUSIONS: EQ-5D-5L item scores and preference score can be estimated from PROMIS health domains and the PROPr. The IRT-based crosswalk is recommended to convert scores between the two measures for use with clinical populations. Mapping models are recommended for the general population.