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From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation
BACKGROUND: The KIDSCREEN-10 index and the Child Health Utility 9D (CHU9D) are two recently developed generic instruments for the measurement of health-related quality of life in children and adolescents. Whilst the CHU9D is a preference based instrument developed specifically for application in cos...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243726/ https://www.ncbi.nlm.nih.gov/pubmed/25169558 http://dx.doi.org/10.1186/s12955-014-0134-z |
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author | Chen, Gang Stevens, Katherine Rowen, Donna Ratcliffe, Julie |
author_facet | Chen, Gang Stevens, Katherine Rowen, Donna Ratcliffe, Julie |
author_sort | Chen, Gang |
collection | PubMed |
description | BACKGROUND: The KIDSCREEN-10 index and the Child Health Utility 9D (CHU9D) are two recently developed generic instruments for the measurement of health-related quality of life in children and adolescents. Whilst the CHU9D is a preference based instrument developed specifically for application in cost-utility analyses, the KIDSCREEN-10 is not currently suitable for application in this context. This paper provides an algorithm for mapping the KIDSCREEN-10 index onto the CHU9D utility scores. METHODS: A sample of 590 Australian adolescents (aged 11–17) completed both the KIDSCREEN-10 and the CHU9D. Several econometric models were estimated, including ordinary least squares estimator, censored least absolute deviations estimator, robust MM-estimator and generalised linear model, using a range of explanatory variables with KIDSCREEN-10 items scores as key predictors. The predictive performance of each model was judged using mean absolute error (MAE) and root mean squared error (RMSE). RESULTS: The MM-estimator with stepwise-selected KIDSCREEN-10 items scores as explanatory variables had the best predictive accuracy using MAE, whilst the equivalent ordinary least squares model had the best predictive accuracy using RMSE. CONCLUSIONS: The preferred mapping algorithm (i.e. the MM-estimate with stepwise selected KIDSCREEN-10 item scores as the predictors) can be used to predict CHU9D utility from KIDSCREEN-10 index with a high degree of accuracy. The algorithm may be usefully applied within cost-utility analyses to generate cost per quality adjusted life year estimates where KIDSCREEN-10 data only are available. |
format | Online Article Text |
id | pubmed-4243726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42437262014-11-26 From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation Chen, Gang Stevens, Katherine Rowen, Donna Ratcliffe, Julie Health Qual Life Outcomes Research BACKGROUND: The KIDSCREEN-10 index and the Child Health Utility 9D (CHU9D) are two recently developed generic instruments for the measurement of health-related quality of life in children and adolescents. Whilst the CHU9D is a preference based instrument developed specifically for application in cost-utility analyses, the KIDSCREEN-10 is not currently suitable for application in this context. This paper provides an algorithm for mapping the KIDSCREEN-10 index onto the CHU9D utility scores. METHODS: A sample of 590 Australian adolescents (aged 11–17) completed both the KIDSCREEN-10 and the CHU9D. Several econometric models were estimated, including ordinary least squares estimator, censored least absolute deviations estimator, robust MM-estimator and generalised linear model, using a range of explanatory variables with KIDSCREEN-10 items scores as key predictors. The predictive performance of each model was judged using mean absolute error (MAE) and root mean squared error (RMSE). RESULTS: The MM-estimator with stepwise-selected KIDSCREEN-10 items scores as explanatory variables had the best predictive accuracy using MAE, whilst the equivalent ordinary least squares model had the best predictive accuracy using RMSE. CONCLUSIONS: The preferred mapping algorithm (i.e. the MM-estimate with stepwise selected KIDSCREEN-10 item scores as the predictors) can be used to predict CHU9D utility from KIDSCREEN-10 index with a high degree of accuracy. The algorithm may be usefully applied within cost-utility analyses to generate cost per quality adjusted life year estimates where KIDSCREEN-10 data only are available. BioMed Central 2014-08-29 /pmc/articles/PMC4243726/ /pubmed/25169558 http://dx.doi.org/10.1186/s12955-014-0134-z Text en © Chen et al.; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Chen, Gang Stevens, Katherine Rowen, Donna Ratcliffe, Julie From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation |
title | From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation |
title_full | From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation |
title_fullStr | From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation |
title_full_unstemmed | From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation |
title_short | From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation |
title_sort | from kidscreen-10 to chu9d: creating a unique mapping algorithm for application in economic evaluation |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243726/ https://www.ncbi.nlm.nih.gov/pubmed/25169558 http://dx.doi.org/10.1186/s12955-014-0134-z |
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