Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Centre for Health Economics, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
BMC Med Res Methodol. 2022 Oct 26;22(1):277. doi: 10.1186/s12874-022-01762-y.
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.
头痛影响测试（HIT-6）和慢性头痛问卷（CH-QLQ）可衡量与头痛相关的生活质量，但它们不是基于偏好的，因此不能用于为成本效益分析生成健康效用。目前尚无用于将 HIT-6 或 CH-QLQ 与基于偏好的慢性头痛人群健康相关生活质量措施进行映射的既定算法。
我们使用直接和响应映射方法，为 HIT-6 和 CHQLQ 开发了生成 EQ-5D-5L 和 SF-6D 效用的算法。使用多阶段模型选择过程来评估模型的预测准确性。所估计的映射算法用于生成英国关税，并使用慢性头痛教育和自我管理研究（CHESS）试验数据集进行验证。
开发了几种在这种情况下可以合理准确地预测健康效用的模型。从 HIT-6 分数预测 EQ-5D-5L 效用得分的最佳表现模型是包含 HIT-6 分数作为协变量的 Censored Least Absolute Deviations（CLAD）（1）模型（均方误差（MSE）为 0.0550）。用于 CH-QLQ 到 EQ-5D-5L 的选定模型是包含 CH-QLQ 总结得分、年龄和性别、平方项和交互项作为协变量的 CLAD（3）模型（MSE 为 0.0583）。从 HIT-6 分数预测 SF-6D 效用得分的最佳表现模型是包含 HIT-6 分数和年龄和性别作为协变量的 CLAD（2）模型（MSE 为 0.0102）。用于 CH-QLQ 到 SF-6D 的选定模型是包含 CH-QLQ 总结得分、年龄和性别作为协变量的 OLS（2）模型（MSE 为 0.0086）。
开发的算法可用于从缺少基于偏好的健康相关生活质量数据的两个头痛专用问卷中估算 EQ-5D-5L 和 SF-6D 效用。然而，需要进一步的工作来帮助定义在头痛研究中衡量健康效用的最佳方法。
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