Mapping between headache specific and generic preference-based health-related quality of life measures
<p><strong>Background</strong></p> 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....
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
BioMed Central
2022
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| _version_ | 1826309244574498816 |
|---|---|
| author | Khan, K Mistry, H Matharu, M Norman, C Petrou, S Stewart, K Underwood, M Achana, F |
| author_facet | Khan, K Mistry, H Matharu, M Norman, C Petrou, S Stewart, K Underwood, M Achana, F |
| author_sort | Khan, K |
| collection | OXFORD |
| description | <p><strong>Background</strong></p>
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.
<p><strong>Methods</strong></p>
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.
<p><strong>Results</strong></p>
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).
<p><strong>Conclusion</strong></p>
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. |
| first_indexed | 2024-03-07T07:31:16Z |
| format | Journal article |
| id | oxford-uuid:012e8de8-f30f-4837-b1f4-1b6144b8e39c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:31:16Z |
| publishDate | 2022 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | oxford-uuid:012e8de8-f30f-4837-b1f4-1b6144b8e39c2023-02-02T12:09:02ZMapping between headache specific and generic preference-based health-related quality of life measuresJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:012e8de8-f30f-4837-b1f4-1b6144b8e39cEnglishSymplectic ElementsBioMed Central2022Khan, KMistry, HMatharu, MNorman, CPetrou, SStewart, KUnderwood, MAchana, F<p><strong>Background</strong></p> 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. <p><strong>Methods</strong></p> 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. <p><strong>Results</strong></p> 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). <p><strong>Conclusion</strong></p> 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. |
| spellingShingle | Khan, K Mistry, H Matharu, M Norman, C Petrou, S Stewart, K Underwood, M Achana, F 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 |
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