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Mapping the neck disability index to SF-6D in patients with chronic neck pain

BACKGROUND: This study sought to statistically map the neck disability index (NDI) to the six-dimension health state short form (SF-6D) to estimate algorithms for use in economic analyses in patients with chronic neck pain (CNP). METHODS: The relationships between NDI and SF-6D scores were estimated...

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Autores principales: Zheng, Yongjun, Tang, Kun, Ye, Le, Ai, Zisheng, Wu, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754827/
https://www.ncbi.nlm.nih.gov/pubmed/26879341
http://dx.doi.org/10.1186/s12955-016-0422-x
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author Zheng, Yongjun
Tang, Kun
Ye, Le
Ai, Zisheng
Wu, Bin
author_facet Zheng, Yongjun
Tang, Kun
Ye, Le
Ai, Zisheng
Wu, Bin
author_sort Zheng, Yongjun
collection PubMed
description BACKGROUND: This study sought to statistically map the neck disability index (NDI) to the six-dimension health state short form (SF-6D) to estimate algorithms for use in economic analyses in patients with chronic neck pain (CNP). METHODS: The relationships between NDI and SF-6D scores were estimated by using data from a cohort of patients with chronic neck pain (n = 272). By using ordinary least squares (OLS), generalized linear modeling (GLM), censored least absolute deviations (CLAD) and Tobit regression, scores from all 10 items of the NDI instruments were univariately tested against SF-6D values and retained in a multivariate regression model, if statistically significant. The predictive ability of the model was assessed by mean absolute error (MAE), root mean square error (RMSE) and normalized RMSE. RESULTS: The mean age of the 272 CNP patients was 39.9 ± 12.3 years; 57.8 % of the CNP patients were female. An OLS regression equation that included recreation item of NDI was optimal, with a MAE of 0.04and 0.04 and an RMSE of 0.06and 0.05in the derivation set and validation set, respectively. Predicted utilities accurately represented the observed ones. CONCLUSIONS: We have provided algorithms for the estimation of health state utility values from the response of NDI. Future economic evaluations of the interventions for chronic neck pain could be informed by these algorithms.
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spelling pubmed-47548272016-02-17 Mapping the neck disability index to SF-6D in patients with chronic neck pain Zheng, Yongjun Tang, Kun Ye, Le Ai, Zisheng Wu, Bin Health Qual Life Outcomes Research BACKGROUND: This study sought to statistically map the neck disability index (NDI) to the six-dimension health state short form (SF-6D) to estimate algorithms for use in economic analyses in patients with chronic neck pain (CNP). METHODS: The relationships between NDI and SF-6D scores were estimated by using data from a cohort of patients with chronic neck pain (n = 272). By using ordinary least squares (OLS), generalized linear modeling (GLM), censored least absolute deviations (CLAD) and Tobit regression, scores from all 10 items of the NDI instruments were univariately tested against SF-6D values and retained in a multivariate regression model, if statistically significant. The predictive ability of the model was assessed by mean absolute error (MAE), root mean square error (RMSE) and normalized RMSE. RESULTS: The mean age of the 272 CNP patients was 39.9 ± 12.3 years; 57.8 % of the CNP patients were female. An OLS regression equation that included recreation item of NDI was optimal, with a MAE of 0.04and 0.04 and an RMSE of 0.06and 0.05in the derivation set and validation set, respectively. Predicted utilities accurately represented the observed ones. CONCLUSIONS: We have provided algorithms for the estimation of health state utility values from the response of NDI. Future economic evaluations of the interventions for chronic neck pain could be informed by these algorithms. BioMed Central 2016-02-16 /pmc/articles/PMC4754827/ /pubmed/26879341 http://dx.doi.org/10.1186/s12955-016-0422-x Text en © Zheng et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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
Zheng, Yongjun
Tang, Kun
Ye, Le
Ai, Zisheng
Wu, Bin
Mapping the neck disability index to SF-6D in patients with chronic neck pain
title Mapping the neck disability index to SF-6D in patients with chronic neck pain
title_full Mapping the neck disability index to SF-6D in patients with chronic neck pain
title_fullStr Mapping the neck disability index to SF-6D in patients with chronic neck pain
title_full_unstemmed Mapping the neck disability index to SF-6D in patients with chronic neck pain
title_short Mapping the neck disability index to SF-6D in patients with chronic neck pain
title_sort mapping the neck disability index to sf-6d in patients with chronic neck pain
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754827/
https://www.ncbi.nlm.nih.gov/pubmed/26879341
http://dx.doi.org/10.1186/s12955-016-0422-x
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