重缩值集回归:使EQ-5D-5L和EQ-5D-3L之间的非参数交叉路口更加透明
1Manchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, United Kingdom.
概括
重缩值集回归为从EQ-5D-3L数据中估计EQ-5D-5L值提供了更透明的方法. 这种技术与非参数横行交叉技术一起,在不同国家产生相同的健康状况值.
科学领域:
- 卫生经济学 卫生经济学
- 心理测量 心理测量 心理测量
- 生物统计学 生物统计学
背景情况:
- EQ-5D是一个广泛使用的患者报告的结果指标,用于评估健康状况.
- 从现有的三级版本 (EQ-5D-3L) 值集中估计五级版本 (EQ-5D-5L) 的值,对于一致的健康经济评估至关重要.
- 目前的方法,如非参数行人交叉,在透明度上有局限性.
研究的目的:
- 引入和评估重新缩放的值集回归作为从EQ-5D-3L值集中导出EQ-5D-5L健康状态值的替代方法.
- 为了比较重新缩放的价值设置回归与非参数横行交叉方法的性能和透明度.
主要方法:
- 通过将EQ-5D-3L回归参数调整为五级框架,开发了重新缩放的值集回归.
- 重缩值集回归和非参数交叉都应用于来自英国,荷兰和西班牙的EQ-5D-3L值集.
- 估计了28个常见的EQ-5D-5L配置文件的健康状况值,并在两种方法之间和不同国家之间进行了比较.
主要成果:
- 重缩值集回归显示了EQ-5D-3L和EQ-5D-5L水平之间的明显对应 (例如,3L水平2到5L水平3).
- 这两种方法都为所选的国家产生了相同的EQ-5D-5L健康状况值,表明一致性.
- 分析证实,重新缩放的值集回归是一种可行的替代非参数交叉路口的可行选择.
结论:
- 与非参数交叉相比,重新缩放的值集回归提供了更好的透明度,用于从EQ-5D-3L值集中估计EQ-5D-5L值.
- 这两种方法都可以在缺乏新的EQ-5D-5L估值研究但拥有相关EQ-5D-3L数据的环境中使用.
- 这有助于使用现有价值集进行更可靠的健康经济评估.
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