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Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D

OBJECTIVE: To help facilitate economic evaluations of oncology treatments, we mapped responses on cancer-specific instrument to generic preference-based measures. METHODS: Cancer patients (n = 367) completed one cancer-specific instrument, the FACT-G, and two preference-based measures, the EQ-5D and...

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Autores principales: Teckle, Paulos, McTaggart-Cowan, Helen, Van der Hoek, Kim, Chia, Stephen, Melosky, Barb, Gelmon, Karen, Peacock, Stuart
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4220776/
https://www.ncbi.nlm.nih.gov/pubmed/24289488
http://dx.doi.org/10.1186/1477-7525-11-203
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author Teckle, Paulos
McTaggart-Cowan, Helen
Van der Hoek, Kim
Chia, Stephen
Melosky, Barb
Gelmon, Karen
Peacock, Stuart
author_facet Teckle, Paulos
McTaggart-Cowan, Helen
Van der Hoek, Kim
Chia, Stephen
Melosky, Barb
Gelmon, Karen
Peacock, Stuart
author_sort Teckle, Paulos
collection PubMed
description OBJECTIVE: To help facilitate economic evaluations of oncology treatments, we mapped responses on cancer-specific instrument to generic preference-based measures. METHODS: Cancer patients (n = 367) completed one cancer-specific instrument, the FACT-G, and two preference-based measures, the EQ-5D and SF-6D. Responses were randomly divided to form development (n = 184) and cross-validation (n = 183) samples. Relationships between the instruments were estimated using ordinary least squares (OLS), generalized linear models (GLM), and censored least absolute deviations (CLAD) regression approaches. The performance of each model was assessed in terms of how well the responses to the cancer-specific instrument predicted EQ-5D and SF-6D utilities using mean absolute error (MAE) and root mean squared error (RMSE). RESULTS: Physical, functional, and emotional well-being domain scores of the FACT-G best explained the EQ-5D and SF-6D. In terms of accuracy of prediction as measured in RMSE, the CLAD model performed best for the EQ-5D (RMSE = 0.095) whereas the GLM model performed best for the SF-6D (RMSE = 0.061). The GLM predicted SF-6D scores matched the observed values more closely than the CLAD and OLS. CONCLUSION: Our results demonstrate that the estimation of both EQ-5D and SF-6D utility indices using the FACT-G responses can be achieved. The CLAD model for the EQ-5D and the GLM model for the SF-6D are recommended. Thus, it is possible to estimate quality-adjusted life years for economic evaluation from studies where only cancer-specific instrument have been administered.
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spelling pubmed-42207762014-11-06 Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D Teckle, Paulos McTaggart-Cowan, Helen Van der Hoek, Kim Chia, Stephen Melosky, Barb Gelmon, Karen Peacock, Stuart Health Qual Life Outcomes Research OBJECTIVE: To help facilitate economic evaluations of oncology treatments, we mapped responses on cancer-specific instrument to generic preference-based measures. METHODS: Cancer patients (n = 367) completed one cancer-specific instrument, the FACT-G, and two preference-based measures, the EQ-5D and SF-6D. Responses were randomly divided to form development (n = 184) and cross-validation (n = 183) samples. Relationships between the instruments were estimated using ordinary least squares (OLS), generalized linear models (GLM), and censored least absolute deviations (CLAD) regression approaches. The performance of each model was assessed in terms of how well the responses to the cancer-specific instrument predicted EQ-5D and SF-6D utilities using mean absolute error (MAE) and root mean squared error (RMSE). RESULTS: Physical, functional, and emotional well-being domain scores of the FACT-G best explained the EQ-5D and SF-6D. In terms of accuracy of prediction as measured in RMSE, the CLAD model performed best for the EQ-5D (RMSE = 0.095) whereas the GLM model performed best for the SF-6D (RMSE = 0.061). The GLM predicted SF-6D scores matched the observed values more closely than the CLAD and OLS. CONCLUSION: Our results demonstrate that the estimation of both EQ-5D and SF-6D utility indices using the FACT-G responses can be achieved. The CLAD model for the EQ-5D and the GLM model for the SF-6D are recommended. Thus, it is possible to estimate quality-adjusted life years for economic evaluation from studies where only cancer-specific instrument have been administered. BioMed Central 2013-12-01 /pmc/articles/PMC4220776/ /pubmed/24289488 http://dx.doi.org/10.1186/1477-7525-11-203 Text en Copyright © 2013 Teckle 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
Teckle, Paulos
McTaggart-Cowan, Helen
Van der Hoek, Kim
Chia, Stephen
Melosky, Barb
Gelmon, Karen
Peacock, Stuart
Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D
title Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D
title_full Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D
title_fullStr Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D
title_full_unstemmed Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D
title_short Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D
title_sort mapping the fact-g cancer-specific quality of life instrument to the eq-5d and sf-6d
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4220776/
https://www.ncbi.nlm.nih.gov/pubmed/24289488
http://dx.doi.org/10.1186/1477-7525-11-203
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