School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK.
Appl Health Econ Health Policy. 2019 Jun;17(3):295-313. doi: 10.1007/s40258-019-00467-6.
Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values (HSUVs) from data obtained from another health-related quality of life (HRQoL) measure. There have been several methodological developments in this area since a previous review up to 2007.
To provide an updated review of all mapping studies that map from HRQoL measures to target generic preference-based measures (EQ-5D measures, SF-6D, HUI measures, QWB, AQoL measures, 15D/16D/17D, CHU-9D) published from January 2007 to October 2018.
A systematic review of English language articles using a variety of approaches: searching electronic and utilities databases, citation searching, targeted journal and website searches.
Full papers of studies that mapped from one health measure to a target preference-based measure using formal statistical regression techniques.
Undertaken by four authors using predefined data fields including measures, data used, econometric models and assessment of predictive ability.
There were 180 papers with 233 mapping functions in total. Mapping functions were generated to obtain EQ-5D-3L/EQ-5D-5L-EQ-5D-Y (n = 147), SF-6D (n = 45), AQoL-4D/AQoL-8D (n = 12), HUI2/HUI3 (n = 13), 15D (n = 8) CHU-9D (n = 4) and QWB-SA (n = 4) HSUVs. A large number of different regression methods were used with ordinary least squares (OLS) still being the most common approach (used ≥ 75% times within each preference-based measure). The majority of studies assessed the predictive ability of the mapping functions using mean absolute or root mean squared errors (n = 192, 82%), but this was lower when considering errors across different categories of severity (n = 92, 39%) and plots of predictions (n = 120, 52%).
The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions.
映射是一种越来越常见的方法，用于从另一种健康相关生活质量（HRQoL）测量中获得的数据预测特定仪器的偏好基础健康状态效用值（HSUVs）。自 2007 年之前的综述以来，该领域已经有了几项方法上的发展。
提供 2007 年 1 月至 2018 年 10 月期间发表的所有从 HRQoL 测量映射到目标通用偏好测量（EQ-5D 测量、SF-6D、HUI 测量、QWB、AQoL 测量、15D/16D/17D、CHU-9D）的映射研究的更新综述。
使用多种方法进行的英语文献系统评价：搜索电子和效用数据库、引文搜索、目标期刊和网站搜索。
使用正式统计回归技术从一种健康测量映射到目标偏好测量的完整论文。
由四位作者使用预定义的数据字段进行，包括测量、使用的数据、计量经济学模型和预测能力评估。
共有 180 篇论文，共有 233 个映射函数。生成映射函数以获得 EQ-5D-3L/EQ-5D-5L-EQ-5D-Y（n=147）、SF-6D（n=45）、AQoL-4D/AQoL-8D（n=12）、HUI2/HUI3（n=13）、15D（n=8）、CHU-9D（n=4）和 QWB-SA（n=4）HSUVs。使用了大量不同的回归方法，普通最小二乘法（OLS）仍然是最常见的方法（在每个偏好测量中使用≥75%的次数）。大多数研究使用平均绝对或均方根误差评估映射函数的预测能力（n=192，82%），但当考虑不同严重程度类别的误差（n=92，39%）和预测图（n=120，52%）时，预测能力较低。
过去 10 年，映射研究的数量大幅增加，并且在方法上有一些证据表明已经取得了进展，考虑到 OLS 以外的模型，并更频繁地报告映射函数的预测能力。
