Mapping EORTC QLQ-C30 and FACT-G onto EQ-5D-5L index for patients with cancer
- PDF / 1,002,072 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 95 Downloads / 194 Views
Open Access
RESEARCH
Mapping EORTC QLQ‑C30 and FACT‑G onto EQ‑5D‑5L index for patients with cancer Yasuhiro Hagiwara1* , Takeru Shiroiwa2, Naruto Taira3, Takuya Kawahara4, Keiko Konomura2, Shinichi Noto5, Takashi Fukuda2 and Kojiro Shimozuma6
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 QLQC30 or FACT-G in cost-effectiveness analyses, whose predictive performance would be similar to or better than those of previous algorithms. Keywords: Cancer, EORTC QLQ-C30, EQ-5D-5L, FACT-G, Mapping, Preference-based measure Background Cancer is a common disease in many countries in the twenty-first century; there were estimated 18.1 million new cancer cases and 9.6 million cancer deaths in 2018 [1]. Although the advancements in cancer treatment prolong life and improve the quality of life of patients, the fight against cancer seems to have a long way to go. One recent problem related to cancer treatments is their cost. *Correspondence: [email protected]‑tokyo.ac.jp 1 Department of Biostatistics, Division of Health Sciences and Nursing, The University of Tokyo, 7‑3‑1, Hongo, Bunkyo‑ku, Tokyo 113‑0033, Japan Full list of author information is available at the end of the article
In the era of targeted, immune, and gene therapies, some treatments are highly effective but costly [2]. With limited medical resources, we need to evaluate not only the effectiveness of cancer treatments but also their costeffectiveness [3]. In cost-effectiveness analyses of cancer treatments, the most important and commonly used health outcome is quality-adjusted life year (QALY). QALY incorpo
Data Loading...
