Scholarlink

Background <p>Health utility measurement is critical for cost-utility analysis (CUA). However, clinical studies often lack direct utility data, and methods of measuring health utility vary across different periods and regions, resulting in missing utility information. Mapping, a&#xa0;method employed&#xa0;to&#xa0;infer&#xa0;utility when&#xa0;direct&#xa0;utility data is&#xa0;absent, has been widely applied in recent years but still faces challenges. This systematic review evaluates the current landscape, trends, and limitations of mapping studies and provides recommendations for improving study design and reporting standards.</p> Methods <p>A literature search was conducted in PubMed, Web of Science, and the HERC Database of Mapping Studies from 2018 to 2024. The study extracted information from the included studies and performed quality assessments based on existing mapping guidelines.</p> Results <p>One hundred thirty-one studies were included, 92 focused on Mapping as the primary objective, five were reviews, 13 focused on methodology, and 21 focused on economic evaluation. The source measure, target measure, and models in mapping functions development remain EORTC-QLQ-C30 (<i>n</i> = 13), EQ-5D (<i>n</i> = 79), and OLS (<i>n</i> = 93). Studies now use larger sample sizes and a higher proportion of response Mapping, and model applications have increased in diversity. However, 32 studies still lack conceptual overlap analysis, and only 16 studies with repeated measurements addressed its issue. Additionally, two studies did not report the region of the sample and value set, while 27 had inconsistencies between the two regions.</p> Conclusions <p>In the last seven years, mapping studies have improved sample size, model application, and result reporting. However, limitations remain in analysing conceptual overlap, ensuring alignment between the sample and value set regions, and processing repeated measurements. To advance the quality of mapping studies, it is necessary to update mapping guidelines and ensure that all mapping functions are developed in compliance with them.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Mapping to estimate health state utility: a systematic review of development and limitations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月16日

查看全文
  • Qiang Su,
  • Xiaochen Peng,
  • Yumeng Zhang,
  • Lihua Sun

摘要

Background

Health utility measurement is critical for cost-utility analysis (CUA). However, clinical studies often lack direct utility data, and methods of measuring health utility vary across different periods and regions, resulting in missing utility information. Mapping, a method employed to infer utility when direct utility data is absent, has been widely applied in recent years but still faces challenges. This systematic review evaluates the current landscape, trends, and limitations of mapping studies and provides recommendations for improving study design and reporting standards.

Methods

A literature search was conducted in PubMed, Web of Science, and the HERC Database of Mapping Studies from 2018 to 2024. The study extracted information from the included studies and performed quality assessments based on existing mapping guidelines.

Results

One hundred thirty-one studies were included, 92 focused on Mapping as the primary objective, five were reviews, 13 focused on methodology, and 21 focused on economic evaluation. The source measure, target measure, and models in mapping functions development remain EORTC-QLQ-C30 (n = 13), EQ-5D (n = 79), and OLS (n = 93). Studies now use larger sample sizes and a higher proportion of response Mapping, and model applications have increased in diversity. However, 32 studies still lack conceptual overlap analysis, and only 16 studies with repeated measurements addressed its issue. Additionally, two studies did not report the region of the sample and value set, while 27 had inconsistencies between the two regions.

Conclusions

In the last seven years, mapping studies have improved sample size, model application, and result reporting. However, limitations remain in analysing conceptual overlap, ensuring alignment between the sample and value set regions, and processing repeated measurements. To advance the quality of mapping studies, it is necessary to update mapping guidelines and ensure that all mapping functions are developed in compliance with them.