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| Home > Publications database > Exploring the Sensitivity of Utilities Predicted by the EQ-5D, SF-6D, HUI3, 15D, AQoL-4D, and AQoL-8D Multi-Attribute Utility Instruments to SF-36 Dimensions. |
| Home > Publications database > Exploring the Sensitivity of Utilities Predicted by the EQ-5D, SF-6D, HUI3, 15D, AQoL-4D, and AQoL-8D Multi-Attribute Utility Instruments to SF-36 Dimensions. |
| Journal Article | DKFZ-2026-00870 |
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2026
Springer International Publishing
[Cham]
Abstract: Multi-attribute utility instruments (MAUIs) are commonly used in health economics to measure health-related quality of life (HRQoL), yet their sensitivity to different health domains varies. This study examines the sensitivity of six widely used MAUIs-EQ-5D, SF-6D, HUI3, 15D, AQoL-4D, and AQoL-8D-to the eight dimensions of the SF-36 survey.We analyzed the associations between SF-36 dimensions and utility scores generated by each MAUI using regression models, focusing on the eight SF-36 domains as predictors. Our analyses used data from the Multi-Instrument Comparison (MIC) project, a cross-national project comprising 8022 respondents from Australia, Canada, Germany, Norway, the United Kingdom, and the United States, including both general population participants and individuals with a range of chronic health conditions. The sensitivity of each instrument was further evaluated through simulations of health interventions targeting mental health, pain relief, stress management, and post-surgical recovery.The analysis revealed distinct sensitivity patterns across instruments. The SF-6D and 15D were particularly sensitive to social and role-oriented domains, while EQ-5D and HUI3 demonstrated greater sensitivity to physical health dimensions, especially bodily pain and physical functioning. AQoL-4D and AQoL-8D showed strong sensitivity to mental health, indicating their suitability for mental health-focused interventions. Regression models identified mental health, bodily pain, and physical functioning as the primary predictors of utility scores across MAUIs. Simulation results further highlighted that mental health interventions were best captured by AQoL-8D and HUI3, while EQ-5D and HUI3 were most sensitive to pain-focused interventions.The choice of MAUI significantly impacts observed HRQoL outcomes depending on the health domains most relevant to the intervention. This study underscores the need for careful instrument selection in HRQoL assessments to ensure alignment with specific health contexts. Future research should validate these sensitivity patterns across diverse populations and clinical applications to optimize MAUI selection in health economic evaluations.
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