Department of Biostatistics, Division of Health Sciences and Nursing, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Center for Outcomes Research and Economic Evaluation for Health, National Institute of Public Health, Wako, Japan.
Health Qual Life Outcomes. 2020 Nov 3;18(1):354. doi: 10.1186/s12955-020-01611-w.
To develop direct and indirect (response) mapping algorithms from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and the Functional Assessment of Cancer Therapy General (FACT-G) onto the EQ-5D-5L index.
We conducted the QOL-MAC study where EQ-5D-5L, EORTC QLQ-C30, and FACT-G were cross-sectionally evaluated in patients receiving drug treatment for solid tumors in Japan. We developed direct and indirect mapping algorithms using 7 regression methods. Direct mapping was based on the Japanese value set. We evaluated the predictive performances based on root mean squared error (RMSE), mean absolute error, and correlation between the observed and predicted EQ-5D-5L indexes.
Based on data from 903 and 908 patients for EORTC QLQ-C30 and FACT-G, respectively, we recommend two-part beta regression for direct mapping and ordinal logistic regression for indirect mapping for both EORTC QLQ-C30 and FACT-G. Cross-validated RMSE were 0.101 in the two methods for EORTC QLQ-C30, whereas they were 0.121 in two-part beta regression and 0.120 in ordinal logistic regression for FACT-G. The mean EQ-5D-5L index and cumulative distribution function simulated from the recommended mapping algorithms generally matched with the observed ones except for very good health (both source measures) and poor health (only FACT-G).
The developed mapping algorithms can be used to generate the EQ-5D-5L index from EORTC QLQ-C30 or FACT-G in cost-effectiveness analyses, whose predictive performance would be similar to or better than those of previous algorithms.
开发直接和间接（反应）映射算法，从欧洲癌症研究和治疗组织生活质量问卷核心 30 项（EORTC QLQ-C30）和癌症治疗功能评估一般（FACT-G）到 EQ-5D-5L 指数。
我们进行了 QOL-MAC 研究，在日本接受实体瘤药物治疗的患者中，横断评估了 EQ-5D-5L、EORTC QLQ-C30 和 FACT-G。我们使用 7 种回归方法开发了直接和间接映射算法。直接映射基于日本价值体系。我们根据均方根误差（RMSE）、平均绝对误差和观察到的与预测的 EQ-5D-5L 指数之间的相关性来评估预测性能。
基于 EORTC QLQ-C30 和 FACT-G 分别为 903 名和 908 名患者的数据，我们建议对 EORTC QLQ-C30 采用两部分贝塔回归进行直接映射，对 FACT-G 采用有序逻辑回归进行间接映射。EORTC QLQ-C30 的两种方法的交叉验证 RMSE 分别为 0.101，而两部分贝塔回归和有序逻辑回归的 FACT-G 分别为 0.121。从推荐的映射算法模拟的平均 EQ-5D-5L 指数和累积分布函数与观察值基本匹配，除了非常好的健康状况（两个来源指标）和较差的健康状况（仅 FACT-G）。
开发的映射算法可用于在成本效益分析中从 EORTC QLQ-C30 或 FACT-G 生成 EQ-5D-5L 指数，其预测性能与以前的算法相似或更好。
