University of Sheffield, Sheffield, UK.
Value Health. 2012 Jul-Aug;15(5):708-15. doi: 10.1016/j.jval.2012.02.008. Epub 2012 May 24.
A five-level version of the EuroQol five-dimensional (EQ-5D) descriptive system (EQ-5D-5L) has been developed, but value sets based on preferences directly elicited from representative general population samples are not yet available. The objective of this study was to develop values sets for the EQ-5D-5L by means of a mapping ("crosswalk") approach to the currently available three-level version of the EQ-5D (EQ-5D-3L) values sets.
The EQ-5D-3L and EQ-5D-5L descriptive systems were coadministered to respondents with conditions of varying severity to ensure a broad range of levels of health across EQ-5D questionnaire dimensions. We explored four models to generate value sets for the EQ-5D-5L: linear regression, nonparametric statistics, ordered logistic regression, and item-response theory. Criteria for the preferred model included theoretical background, statistical fit, predictive power, and parsimony.
A total of 3691 respondents were included. All models had similar fit statistics. Predictive power was slightly better for the nonparametric and ordered logistic regression models. In considering all criteria, the nonparametric model was selected as most suitable for generating values for the EQ-5D-5L.
The nonparametric model was preferred for its simplicity while performing similarly to the other models. Being independent of the value set that is used, it can be applied to transform any EQ-5D-3L value set into EQ-5D-5L index values. Strengths of this approach include compatibility with three-level value sets. A limitation of any crosswalk is that the range of index values is restricted to the range of the EQ-5D-3L value sets.
已经开发出了五水平版欧洲五维健康量表(EQ-5D)描述系统(EQ-5D-5L),但基于代表性普通人群样本直接得出的偏好的价值体系尚不可用。本研究的目的是通过与目前可用的三水平版 EQ-5D(EQ-5D-3L)价值体系的映射(“交叉映射”)方法来开发 EQ-5D-5L 的价值体系。
EQ-5D-3L 和 EQ-5D-5L 描述系统共同应用于病情严重程度不同的患者,以确保 EQ-5D 问卷维度的健康水平涵盖广泛的范围。我们探索了四种生成 EQ-5D-5L 价值体系的模型:线性回归、非参数统计、有序逻辑回归和项目反应理论。首选模型的标准包括理论背景、统计拟合、预测能力和简约性。
共纳入 3691 名患者。所有模型的拟合统计数据都相似。非参数和有序逻辑回归模型的预测能力稍好。在考虑所有标准的情况下,选择非参数模型作为最适合生成 EQ-5D-5L 价值体系的模型。
非参数模型因其简单性而被选中,同时与其他模型表现相似。它独立于所使用的价值体系,可以应用于将任何 EQ-5D-3L 价值体系转换为 EQ-5D-5L 指数值。这种方法的优势包括与三水平价值体系的兼容性。任何交叉映射的局限性在于指数值的范围仅限于 EQ-5D-3L 价值体系的范围。