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Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients
BACKGROUND: 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. Altho...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3542092/ https://www.ncbi.nlm.nih.gov/pubmed/23244763 http://dx.doi.org/10.1186/1477-7525-10-151 |
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author | Kim, Seon Ha Jo, Min-Woo Kim, Hwa-Jung Ahn, Jin-Hee |
author_facet | Kim, Seon Ha Jo, Min-Woo Kim, Hwa-Jung Ahn, Jin-Hee |
author_sort | Kim, Seon Ha |
collection | PubMed |
description | BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: This mapping algorithm enabled the EORTC QLQ-C30 to be converted to the EQ-5D utility index to assess cancer patients in Korea. |
format | Online Article Text |
id | pubmed-3542092 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35420922013-01-11 Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients Kim, Seon Ha Jo, Min-Woo Kim, Hwa-Jung Ahn, Jin-Hee Health Qual Life Outcomes Research BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: This mapping algorithm enabled the EORTC QLQ-C30 to be converted to the EQ-5D utility index to assess cancer patients in Korea. BioMed Central 2012-12-17 /pmc/articles/PMC3542092/ /pubmed/23244763 http://dx.doi.org/10.1186/1477-7525-10-151 Text en Copyright ©2012 Kim et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 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. |
spellingShingle | Research Kim, Seon Ha Jo, Min-Woo Kim, Hwa-Jung Ahn, Jin-Hee Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients |
title | Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients |
title_full | Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients |
title_fullStr | Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients |
title_full_unstemmed | Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients |
title_short | Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients |
title_sort | mapping eortc qlq-c30 onto eq-5d for the assessment of cancer patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3542092/ https://www.ncbi.nlm.nih.gov/pubmed/23244763 http://dx.doi.org/10.1186/1477-7525-10-151 |
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