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01.12.2012 | Research | Ausgabe 1/2012 Open Access

Health and Quality of Life Outcomes 1/2012

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

Health and Quality of Life Outcomes > Ausgabe 1/2012
Seon Ha Kim, Min-Woo Jo, Hwa-Jung Kim, Jin-Hee Ahn
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1477-7525-10-151) contains supplementary material, which is available to authorized users.

Competing interests

The authors have no disclosures.

Authors’ contributions

All authors contributed to the conception and design of the study, the acquisition of data, and the interpretation of the results. SHK analyzed the data and was involved in drafting the manuscript; MWJ, HJK and JHA were involved in revising the manuscript to ensure its critically important content. All authors have read and approved the final manuscript.



The 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.


The 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).


A 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.


This mapping algorithm enabled the EORTC QLQ-C30 to be converted to the EQ-5D utility index to assess cancer patients in Korea.
Authors’ original file for figure 1
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