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

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Medical Decision Making Pub Date : 2025-08-01 Epub Date: 2025-06-13 DOI:10.1177/0272989X251340990
Xiaodan Tang, Ron D Hays, David Cella, Sarah Acaster, Benjamin David Schalet, Asia Sikora Kessler, Montserrat Vera Llonch, Janel Hanmer
{"title":"Mapping and Linking between the EQ-5D-5L and the PROMIS Domains in the United States.","authors":"Xiaodan Tang, Ron D Hays, David Cella, Sarah Acaster, Benjamin David Schalet, Asia Sikora Kessler, Montserrat Vera Llonch, Janel Hanmer","doi":"10.1177/0272989X251340990","DOIUrl":null,"url":null,"abstract":"<p><p>ObjectivesThe 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.MethodsA 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.ResultsEQ-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.ConclusionsThe 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.HighlightsThis 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.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"740-752"},"PeriodicalIF":3.1000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12260195/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Decision Making","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/0272989X251340990","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/13 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

ObjectivesThe 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.MethodsA 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.ResultsEQ-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.ConclusionsThe 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.HighlightsThis 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.

Abstract Image

Abstract Image

Abstract Image

美国EQ-5D-5L与PROMIS结构域的映射与连接。
EQ-5D-5L和患者报告结果测量信息系统(PROMIS®)偏好评分(PROPr)是基于偏好的测量方法。本研究比较了将PROPr + Anxiety中包含的PROPr和PROMIS域与EQ-5D-5L项目反应和偏好分数对齐的映射和链接方法。方法对983名成年人进行在线调查。采用基于回归的映射方法和项目反应理论(IRT)链接方法对分数进行对齐。使用映射来预测EQ-5D-5L项目的反应或使用PROMIS域得分的偏好得分。采用相等策略来解决回归均值问题。链接法基于PROMIS得分指标估计EQ-5D-5L的项目参数,生成EQ-5D-5L项目反应与相关PROMIS领域得分之间的双向交叉曲线。结果tq - 5d - 5l项目的回答被焦虑、抑郁、疲劳、疼痛干扰、身体功能、社会角色和睡眠障碍的PROMIS域显著地解释。EQ-5D-5L偏好得分由相同的PROMIS域(不包括焦虑和疲劳)和PROPr偏好得分来解释。在EQ-5D-5L项目反应与身体功能、疼痛和抑郁的PROMIS域之间产生了irt连接的交叉通道。所有3种方法的观察得分和预测得分之间存在微小差异。采用等百分位相等策略的直接映射法(直接预测EQ-5D-5L分数)由于预测精度和可比较分数范围覆盖范围的提高而优于链接法。结论promise -29+2中包含的PROPr和PROMIS结构域预测EQ-5D-5L偏好得分或项目反应。这两种方法都可以产生可接受的精确EQ-5D-5L偏好分数,使用等同策略的直接映射方法提供更好的精度。我们总结了基于可用分数和期望分数的推荐分数转换表。本研究比较了映射(分数预测)和基于ird的链接方法,以将PROPr和PROMIS域与EQ-5D-5L项目反应和偏好分数对齐。研究人员、临床医生和利益相关者可以使用本研究的回归公式和人行横道评分来转换PROMIS和EQ-5D-5L之间的得分。映射可以生成更精确的分数,而链接在收集较少的PROMIS域分数时提供更大的分数估计灵活性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信