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6种多属性效用工具之间的映射

Mapping between 6 Multiattribute Utility Instruments.

作者信息

Chen Gang, Khan Munir A, Iezzi Angelo, Ratcliffe Julie, Richardson Jeff

机构信息

Flinders Health Economics Group, Flinders University, Adelaide, Australia (GC, JR)

Centre for Health Economics, Monash University, Clayton, Australia (MAK, AI, JR)

出版信息

Med Decis Making. 2016 Feb;36(2):160-75. doi: 10.1177/0272989X15578127. Epub 2015 Apr 3.

Abstract

BACKGROUND

Cost-utility analyses commonly employ a multiattribute utility (MAU) instrument to estimate the health state utilities, which are needed to calculate quality-adjusted life years. Different MAU instruments predict significantly different utilities, which makes comparison of results from different evaluation studies problematical.

AIM

This article presents mapping functions ("crosswalks") from 6 MAU instruments (EQ-5D-5L, SF-6D, Health Utilities Index 3 [HUI 3], 15D, Quality of Well-Being [QWB], and Assessment of Quality of Life 8D [AQoL-8D]) to each of the other 5 instruments in the study: a total of 30 mapping functions.

METHODS

Data were obtained from a multi-instrument comparison survey of the public and patients in 7 disease areas conducted in 6 countries (Australia, Canada, Germany, Norway, United Kingdom, and United States). The 8022 respondents were administered each of the 6 study instruments. Mapping equations between each instrument pair were estimated using 4 econometric techniques: ordinary least squares, generalized linear model, censored least absolute deviations, and, for the first time, a robust MM-estimator.

RESULTS

Goodness-of-fit indicators for each of the results are within the range of published studies. Transformations reduced discrepancies between predicted utilities. Incremental utilities, which determine the value of quality-related health benefits, are almost perfectly aligned at the sample means.

CONCLUSION

Transformations presented here align the measurement scales of MAU instruments. Their use will increase confidence in the comparability of evaluation studies, which have employed different MAU instruments.

摘要

背景

成本效用分析通常采用多属性效用(MAU)工具来估计健康状态效用,而计算质量调整生命年需要这些效用值。不同的MAU工具预测的效用值差异显著,这使得不同评估研究结果的比较存在问题。

目的

本文呈现了从6种MAU工具(EQ-5D-5L、SF-6D、健康效用指数3 [HUI 3]、15D、幸福感质量量表[QWB]和生活质量评估8维度量表[AQoL-8D])到本研究中其他5种工具的映射函数(“交叉对照表”):总共30个映射函数。

方法

数据来自在6个国家(澳大利亚、加拿大、德国、挪威、英国和美国)针对7个疾病领域的公众和患者进行的多工具比较调查。8022名受访者接受了6种研究工具的测试。使用4种计量经济学技术估计每对工具之间的映射方程:普通最小二乘法、广义线性模型、截尾最小绝对偏差法,并且首次使用了稳健的MM估计器。

结果

每个结果的拟合优度指标都在已发表研究的范围内。转换减少了预测效用之间的差异。决定与质量相关的健康效益价值的增量效用在样本均值处几乎完全一致。

结论

本文提出的转换使MAU工具的测量尺度保持一致。它们的使用将增加对采用不同MAU工具的评估研究可比性的信心。

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