Department of Statistics, School of Law and Social Sciences, University Carlos III of Madrid, 126-28903, Getafe, Madrid, Spain.
Health Service Research Network on Chronic Diseases (REDISSEC), Madrid, Spain.
Qual Life Res. 2023 Jun;32(6):1785-1794. doi: 10.1007/s11136-023-03351-y. Epub 2023 Feb 3.
Preference-based measures are valuable tools for evaluating therapeutic interventions and for cost-effectiveness studies. Mapping procedures are useful when it is not possible to collect these kind of measures. The objective of this study was to evaluate which mapping method is the most appropriate to estimate the EQ-5D-5L index from the Spanish National Health Survey functional disability scale.
The sample, formed by 5708 older adults (aged 65 years or older), was drawn from the Spanish National Health Survey ("Encuesta Nacional de Salud en España," ENSE in Spanish 2011-2012). The predictions of EQ-5D-5L index were performed with response mapping using Bayesian network (BN), ordered logit (Ologit), and multinomial logistic (ML). The following direct methods were used: ordinary least squares (OLS) and Tobit regression. The intraclass correlation coefficient (ICC), absolute error (MAE), mean squared error (MSE), and root-mean squared error (RMSE) were calculated to compare all models. The predictions of response models were obtained through the expected value method.
BN model showed the highest ICC (0.756, 95% confidence interval, CI 0.733-0.777) and lowest MAE (0.110, 95% CI 0.104-0.115). OLS was the model with worse accuracy results with lowest ICC (0.621, 95% CI 0.553-0.681) and highest MAE (0.159, 95%CI: 0.145-0.173).
Indirect mapping methods (BN, Ologit, and ML) had a better accuracy than the direct methods. The response mapping approach provides a robust method to estimate EQ-5D-5L scores from the functional disability scale.
偏好量表是评估治疗干预措施和成本效益研究的有用工具。当无法收集此类量表时，映射程序很有用。本研究的目的是评估哪种映射方法最适合从西班牙国家健康调查的功能障碍量表中估算 EQ-5D-5L 指数。
该样本由 5708 名老年人（年龄在 65 岁或以上）组成，来自西班牙国家健康调查（“Encuesta Nacional de Salud en España”，简称 ENSE）（2011-2012 年）。使用贝叶斯网络（BN）、有序逻辑（Ologit）和多项逻辑（ML）进行响应映射，预测 EQ-5D-5L 指数。还使用了以下直接方法：普通最小二乘法（OLS）和 Tobit 回归。计算了组内相关系数（ICC）、绝对误差（MAE）、均方误差（MSE）和均方根误差（RMSE），以比较所有模型。通过期望值法获得响应模型的预测。
BN 模型显示出最高的 ICC（0.756，95%置信区间，0.733-0.777）和最低的 MAE（0.110，95%CI 0.104-0.115）。OLS 是准确性结果最差的模型，其 ICC 最低（0.621，95%CI 0.553-0.681），MAE 最高（0.159，95%CI：0.145-0.173）。
间接映射方法（BN、Ologit 和 ML）比直接方法具有更高的准确性。响应映射方法提供了一种从功能障碍量表中估算 EQ-5D-5L 评分的稳健方法。
