将癌症患者专用的 QLQ-C30 量表映射到通用的 EQ-5D-5L 和 SF-6D 量表上。
Mapping the cancer-specific QLQ-C30 onto the generic EQ-5D-5L and SF-6D in colorectal cancer patients.
机构信息
a Department of Health Management and Economics, School of Public Health , Tehran University of Medical Sciences , Tehran , Iran.
b Iranian Center of Excellence in Health Management, School of Management and Medical Informatics, Health Economics Department , Tabriz University of Medical Sciences , Tabriz , Iran.
出版信息
Expert Rev Pharmacoecon Outcomes Res. 2019 Feb;19(1):89-96. doi: 10.1080/14737167.2018.1517046. Epub 2018 Sep 3.
INTRODUCTION
Economic evaluation of healthcare interventions usually needs accurate data on utility and health-related quality-of-life scores. The aim of this study is to map QLQ-C30 scale score onto EQ-5D-5L and SF-6D utility values in colorectal cancer (CRC) patients.
METHODS
EQ-5D-5L, SF-6D, and QLQ-C30 were completed by 252 patients with CRC who were referred to three cancer centers in Tehran between May and September 2017. Moreover, OLS, Tobit, and CLAD models were used to predict EQ-5D-5L and SF-6D values. The goodness of fit of models was evaluated using Pred R and Adj R. In addition, their predictive performance was assessed by MAE, RMSE, ICC, MID, and Spearman's correlation coefficients between observed and predicted EQ-5D-5L and SF-6D values. Models were validated using a 10-fold cross-validation method.
RESULTS
Considering the goodness of fit and predictive ability of models, the OLS Model 2 performed best for EQ-5D-5L (Adj R = 58.09%, Pred R = 58.93%, MAE = 0.0932, RMSE = 0.129) and the OLS Model 3 performed best for SF-6D (Adj R = 54.90%, Pred R = 55.62%, MAE = 0.0485, RMSE = 0.0634).
CONCLUSION
Our results demonstrated that algorithms developed based on OLS Models 1 and 2 are the best for predicted EQ-5D-5L and SF-6D values, respectively.
简介
医疗干预的经济评估通常需要有关效用和健康相关生活质量评分的准确数据。本研究的目的是将 QLQ-C30 量表评分映射到结直肠癌（CRC）患者的 EQ-5D-5L 和 SF-6D 效用值。
方法
2017 年 5 月至 9 月，252 名被转诊至德黑兰三家癌症中心的 CRC 患者完成了 EQ-5D-5L、SF-6D 和 QLQ-C30 问卷。此外，还使用 OLS、Tobit 和 CLAD 模型来预测 EQ-5D-5L 和 SF-6D 值。使用 Pred R 和 Adj R 评估模型的拟合优度。此外，通过观察和预测 EQ-5D-5L 和 SF-6D 值之间的 MAE、RMSE、ICC、MID 和 Spearman 相关系数来评估模型的预测性能。使用 10 折交叉验证方法验证模型。
结果
考虑到模型的拟合优度和预测能力，OLS 模型 2 最适合 EQ-5D-5L（Adj R=58.09%，Pred R=58.93%，MAE=0.0932，RMSE=0.129），OLS 模型 3 最适合 SF-6D（Adj R=54.90%，Pred R=55.62%，MAE=0.0485，RMSE=0.0634）。
结论
我们的结果表明，基于 OLS 模型 1 和 2 开发的算法分别是预测 EQ-5D-5L 和 SF-6D 值的最佳算法。
Zotero 插件