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Mapping between headache specific and generic preference-based health-related quality of life measures

BACKGROUND: The Headache Impact Test (HIT-6) and the Chronic Headache Questionnaire (CH-QLQ) measure headache-related quality of life but are not preference-based and therefore cannot be used to generate health utilities for cost-effectiveness analyses. There are currently no established algorithms...

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Autores principales: Khan, Kamran, Mistry, Hema, Matharu, Manjit, Norman, Chloe, Petrou, Stavros, Stewart, Kimberley, Underwood, Martin, Achana, Felix
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597975/
https://www.ncbi.nlm.nih.gov/pubmed/36289468
http://dx.doi.org/10.1186/s12874-022-01762-y
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author Khan, Kamran
Mistry, Hema
Matharu, Manjit
Norman, Chloe
Petrou, Stavros
Stewart, Kimberley
Underwood, Martin
Achana, Felix
author_facet Khan, Kamran
Mistry, Hema
Matharu, Manjit
Norman, Chloe
Petrou, Stavros
Stewart, Kimberley
Underwood, Martin
Achana, Felix
author_sort Khan, Kamran
collection PubMed
description BACKGROUND: The Headache Impact Test (HIT-6) and the Chronic Headache Questionnaire (CH-QLQ) measure headache-related quality of life but are not preference-based and therefore cannot be used to generate health utilities for cost-effectiveness analyses. There are currently no established algorithms for mapping between the HIT-6 or CH-QLQ and preference-based health-related quality-of-life measures for chronic headache population. METHODS: We developed algorithms for generating EQ-5D-5L and SF-6D utilities from the HIT-6 and the CHQLQ using both direct and response mapping approaches. A multi-stage model selection process was used to assess the predictive accuracy of the models. The estimated mapping algorithms were derived to generate UK tariffs and was validated using the Chronic Headache Education and Self-management Study (CHESS) trial dataset. RESULTS: Several models were developed that reasonably accurately predict health utilities in this context. The best performing model for predicting EQ-5D-5L utility scores from the HIT-6 scores was a Censored Least Absolute Deviations (CLAD) (1) model that only included the HIT-6 score as the covariate (mean squared error (MSE) 0.0550). The selected model for CH-QLQ to EQ-5D-5L was the CLAD (3) model that included CH-QLQ summary scores, age, and gender, squared terms and interaction terms as covariates (MSE 0.0583). The best performing model for predicting SF-6D utility scores from the HIT-6 scores was the CLAD (2) model that included the HIT-6 score and age and gender as covariates (MSE 0.0102). The selected model for CH-QLQ to SF-6D was the OLS (2) model that included CH-QLQ summary scores, age, and gender as covariates (MSE 0.0086). CONCLUSION: The developed algorithms enable the estimation of EQ-5D-5L and SF-6D utilities from two headache-specific questionnaires where preference-based health-related quality of life data are missing. However, further work is needed to help define the best approach to measuring health utilities in headache studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01762-y.
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spelling pubmed-95979752022-10-27 Mapping between headache specific and generic preference-based health-related quality of life measures Khan, Kamran Mistry, Hema Matharu, Manjit Norman, Chloe Petrou, Stavros Stewart, Kimberley Underwood, Martin Achana, Felix BMC Med Res Methodol Research BACKGROUND: The Headache Impact Test (HIT-6) and the Chronic Headache Questionnaire (CH-QLQ) measure headache-related quality of life but are not preference-based and therefore cannot be used to generate health utilities for cost-effectiveness analyses. There are currently no established algorithms for mapping between the HIT-6 or CH-QLQ and preference-based health-related quality-of-life measures for chronic headache population. METHODS: We developed algorithms for generating EQ-5D-5L and SF-6D utilities from the HIT-6 and the CHQLQ using both direct and response mapping approaches. A multi-stage model selection process was used to assess the predictive accuracy of the models. The estimated mapping algorithms were derived to generate UK tariffs and was validated using the Chronic Headache Education and Self-management Study (CHESS) trial dataset. RESULTS: Several models were developed that reasonably accurately predict health utilities in this context. The best performing model for predicting EQ-5D-5L utility scores from the HIT-6 scores was a Censored Least Absolute Deviations (CLAD) (1) model that only included the HIT-6 score as the covariate (mean squared error (MSE) 0.0550). The selected model for CH-QLQ to EQ-5D-5L was the CLAD (3) model that included CH-QLQ summary scores, age, and gender, squared terms and interaction terms as covariates (MSE 0.0583). The best performing model for predicting SF-6D utility scores from the HIT-6 scores was the CLAD (2) model that included the HIT-6 score and age and gender as covariates (MSE 0.0102). The selected model for CH-QLQ to SF-6D was the OLS (2) model that included CH-QLQ summary scores, age, and gender as covariates (MSE 0.0086). CONCLUSION: The developed algorithms enable the estimation of EQ-5D-5L and SF-6D utilities from two headache-specific questionnaires where preference-based health-related quality of life data are missing. However, further work is needed to help define the best approach to measuring health utilities in headache studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01762-y. BioMed Central 2022-10-26 /pmc/articles/PMC9597975/ /pubmed/36289468 http://dx.doi.org/10.1186/s12874-022-01762-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Khan, Kamran
Mistry, Hema
Matharu, Manjit
Norman, Chloe
Petrou, Stavros
Stewart, Kimberley
Underwood, Martin
Achana, Felix
Mapping between headache specific and generic preference-based health-related quality of life measures
title Mapping between headache specific and generic preference-based health-related quality of life measures
title_full Mapping between headache specific and generic preference-based health-related quality of life measures
title_fullStr Mapping between headache specific and generic preference-based health-related quality of life measures
title_full_unstemmed Mapping between headache specific and generic preference-based health-related quality of life measures
title_short Mapping between headache specific and generic preference-based health-related quality of life measures
title_sort mapping between headache specific and generic preference-based health-related quality of life measures
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597975/
https://www.ncbi.nlm.nih.gov/pubmed/36289468
http://dx.doi.org/10.1186/s12874-022-01762-y
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