Mapping EORTC QLQ-C30 and FACT-G onto EQ-5D-5L index for patients with cancer

• Main Authors: Yasuhiro Hagiwara, Takeru Shiroiwa, Naruto Taira, Takuya Kawahara, Keiko Konomura, Shinichi Noto, Takashi Fukuda, Kojiro Shimozuma
• Format: Article
• Language: English
• Published: BMC 2020-11-01
• Series: Health and Quality of Life Outcomes
• Subjects: Cancer; EORTC QLQ-C30; EQ-5D-5L; FACT-G; Mapping; Preference-based measure
• Online Access: http://link.springer.com/article/10.1186/s12955-020-01611-w

Abstract Background 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. Methods 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. Results 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). Conclusions 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.