Mapping between headache specific and generic preference-based health-related quality of life measures.
Headache
Migraine
Quality of Life
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
26 10 2022
26 10 2022
Historique:
received:
13
05
2022
accepted:
13
10
2022
entrez:
27
10
2022
pubmed:
28
10
2022
medline:
29
10
2022
Statut:
epublish
Résumé
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. 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. 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). 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.
Sections du résumé
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.
Identifiants
pubmed: 36289468
doi: 10.1186/s12874-022-01762-y
pii: 10.1186/s12874-022-01762-y
pmc: PMC9597975
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
277Informations de copyright
© 2022. The Author(s).
Références
Qual Life Res. 2010 Feb;19(1):65-80
pubmed: 19941078
Qual Life Res. 2003 Dec;12(8):963-74
pubmed: 14651415
Qual Life Res. 2011 Dec;20(10):1727-36
pubmed: 21479777
Value Health. 2012 Jul-Aug;15(5):708-15
pubmed: 22867780
Eur J Health Econ. 2013 Apr;14(2):231-41
pubmed: 22045272
Health Qual Life Outcomes. 2008 Sep 29;6:73
pubmed: 18823555
BMJ Open. 2020 Apr 12;10(4):e033520
pubmed: 32284387
J Health Econ. 2002 Mar;21(2):271-92
pubmed: 11939242
Med Care. 2004 Sep;42(9):851-9
pubmed: 15319610
Value Health. 2014 Sep;17(6):686-95
pubmed: 25236992
Health Policy. 1990 Dec;16(3):199-208
pubmed: 10109801
Qual Life Res. 2012 Aug;21(6):1065-73
pubmed: 21947656
Headache. 2012 Mar;52(3):409-21
pubmed: 21929662
Eur J Health Econ. 2010 Apr;11(2):215-25
pubmed: 19585162
BMJ. 2004 Mar 27;328(7442):747
pubmed: 15023830
Health Qual Life Outcomes. 2006 Mar 25;4:20
pubmed: 16563170
Med Decis Making. 2006 Jan-Feb;26(1):18-29
pubmed: 16495197
BMJ. 1998 Mar 7;316(7133):736-41
pubmed: 9529408
Med Decis Making. 2011 Nov-Dec;31(6):790-9
pubmed: 22067429
Cephalalgia. 2008 Apr;28(4):334-45
pubmed: 18315686
Health Econ. 2018 Jan;27(1):7-22
pubmed: 28833869
Headache. 1998 Apr;38(4):295-302
pubmed: 9595870
Cephalalgia. 2021 Sep;41(10):1100-1123
pubmed: 33942667