Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients

Health and Quality of Life Outcomes

Research

Hwa-Jung Kim¹  Min-Woo Jo²  Seon Ha Kim²  Jin-Hee Ahn³ 

[1] Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, 86, Asanbyeongwon-gil, 138-736, Songpa-gu, Seoul, Korea;Department of Preventive Medicine, University of Ulsan College of Medicine, 86, Asanbyeongwon-gil, 138-736, Songpa-gu, Seoul, Korea;Division of Oncology, Asan Medical Center, University of Ulsan College of Medicine, 86, Asanbyeongwon-gil, 138-736, Songpa-gu, Seoul, Korea;

关键词: EQ-5D;    EORTC QLQ-C30;    Cancer;    Mapping;    Quality of life;   

DOI  :  10.1186/1477-7525-10-151

 received in 2012-07-27, accepted in 2012-12-13,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundThe European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) is the instrument most frequently used to measure quality of life in cancer patients, whereas the EQ-5D is widely used to measure and evaluate general health status. Although the EORTC QLQ-C30 has been mapped to EQ-5D utilities, those studies were limited to patients with a single type of cancer. The present study aimed to develop a mapping relationship between the EORTC QLQ-C30 and EQ-5D-based utility values at the individual level.MethodsThe model was derived using patients with different types of cancer who were receiving chemotherapy. The external validation set comprised outpatients with colon cancer. Ordinary least squares regression was used to estimate the EQ-5D index from the EORTC QLQ-C30 results. The predictability, goodness of fit, and signs of the estimated coefficients of the model were assessed. Predictive ability was determined by calculating the mean absolute error, the estimated proportions with absolute errors > 0.05 and > 0.1, and the root-mean-squared error (RMSE).ResultsA model that included global health, physical, role, emotional functions, and pain was optimal, with a mean absolute error of 0.069 and an RMSE of 0.095 (normalized RMSE, 8.1%). The explanatory power of this model was 51.6%. The mean absolute error was higher for modeled patients in poor health.ConclusionsThis mapping algorithm enabled the EORTC QLQ-C30 to be converted to the EQ-5D utility index to assess cancer patients in Korea.

【 授权许可】

Unknown   
© Kim et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.