Mapping and Linking between the EQ-5D-5L and the PROMIS Domains in the United States

Xiaodan Tang, Ron D. Hays, David Cella, Sarah Acaster, Benjamin David Schalet, Asia Sikora Kessler, Montserrat Vera Llonch and Janel Hanmer
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Xiaodan Tang: Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
Ron D. Hays: Department of Medicine, Division of General Internal Medicine & Health Services Research, University of California, Los Angeles, CA, USA
David Cella: Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
Sarah Acaster: Acaster Lloyd Consulting Ltd, London, UK
Benjamin David Schalet: Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
Asia Sikora Kessler: Ionis Pharmaceuticals, Carlsbad, CA, USA
Montserrat Vera Llonch: Ionis Pharmaceuticals, Carlsbad, CA, USA
Janel Hanmer: Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

Medical Decision Making, 2025, vol. 45, issue 6, 740-752

Abstract: Objectives The EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS®) preference score (PROPr) are preference-based measures. This study compares mapping and linking approaches to align the PROPr and the PROMIS domains included in PROPr plus Anxiety with EQ-5D-5L item responses and preference scores. Methods A general population sample of 983 adults completed the online survey. Regression-based mapping methods and item response theory (IRT) linking methods were used to align scores. Mapping was used to predict EQ-5D-5L item responses or preference scores using PROMIS domain scores. Equating strategies were applied to address regression to the mean. The linking approach estimated item parameters of EQ-5D-5L based on the PROMIS score metric and generated bidirectional crosswalks between EQ-5D-5L item responses and relevant PROMIS domain scores. Results 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. IRT-linking crosswalks were generated between EQ-5D-5L item responses and PROMIS domains of Physical Function, Pain, and Depression. Small differences were found between observed and predicted scores for all 3 methods. The direct mapping approach (directly predicting EQ-5D-5L scores) with the equipercentile equating strategy proved superior to the linking method due to improved prediction accuracy and comparable score range coverage. Conclusions The PROPr and the PROMIS domains included in the PROMIS-29+2 predict EQ-5D-5L preference scores or item responses. Both methods can generate acceptably precise EQ-5D-5L preference scores, with the direct mapping approach using the equating strategy offering better precision. We summarized recommended score conversion tables based on available and desired scores. Highlights This study compares mapping (score prediction) and IRT-based linking approaches to align the PROPr and the PROMIS domains with EQ-5D-5L item responses and preference scores. Researchers, clinicians, and stakeholders can use this study’s regression formulas and score crosswalks to convert scores between PROMIS and EQ-5D-5L. Mapping can generate more precise scores, while linking offers greater flexibility in score estimation when fewer PROMIS domain scores are collected.

Keywords: EQ-5D-5L; equating; linking; mapping; PROMIS; PROPr (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:45:y:2025:i:6:p:740-752

DOI: 10.1177/0272989X251340990

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