Re-Estimating an EQ-5D-5L Value Set for China
Author(s)
Zhihao Yang, BSc, MSc, PhD1, Nan Luo, PhD2.
1Associate Professor, Jinan University, Gui'an, China, 2National University of Singapore, Singapore, Singapore.
1Associate Professor, Jinan University, Gui'an, China, 2National University of Singapore, Singapore, Singapore.
OBJECTIVES: To update the EQ-5D-5L value set for China using a representative sample from both urban and rural areas, applying the latest EuroQol valuation protocol and improved modelling techniques.
METHODS: We collected time trade-off (cTTO) and discrete choice experiment (DCE) data from 1,206 respondents across 15 regions in China. Sampling quotas were based on age, sex, education, and rural/urban residence to reflect national demographics. Data were collected via face-to-face interviews following the EQ-VT protocol with rigorous quality control. A total of 48 models were tested, and a hybrid cross-attribute level effects (CALE) model was selected based on face validity, prediction accuracy (RMSE, MAE, Lin’s CCC), and parsimony.
RESULTS: Data collection was completed between July and September 2023. The final model used both cTTO and DCE data and achieved an RMSE of 0.043. Compared with the 2012 EQ-5D-5L value set, the new value set showed changes in dimension ranking and disutility magnitude. Pain/discomfort and anxiety/depression became the most important dimensions, while mobility was ranked lower. The worst health state (55555) received a value of -0.661, compared to -0.391 in the original set.
CONCLUSIONS: This study provides an updated EQ-5D-5L value set for China using robust data collection and modelling methods. The new value set reflects more recent societal preferences and corrects limitations of the original study, such as lack of rural representation and quality control. Changes in preferences may be attributed to improved methodology, population diversity, and contextual factors like the COVID-19 pandemic. These findings support regular updates to value sets to maintain their policy relevance and applicability in economic evaluations and health technology assessments in China.
METHODS: We collected time trade-off (cTTO) and discrete choice experiment (DCE) data from 1,206 respondents across 15 regions in China. Sampling quotas were based on age, sex, education, and rural/urban residence to reflect national demographics. Data were collected via face-to-face interviews following the EQ-VT protocol with rigorous quality control. A total of 48 models were tested, and a hybrid cross-attribute level effects (CALE) model was selected based on face validity, prediction accuracy (RMSE, MAE, Lin’s CCC), and parsimony.
RESULTS: Data collection was completed between July and September 2023. The final model used both cTTO and DCE data and achieved an RMSE of 0.043. Compared with the 2012 EQ-5D-5L value set, the new value set showed changes in dimension ranking and disutility magnitude. Pain/discomfort and anxiety/depression became the most important dimensions, while mobility was ranked lower. The worst health state (55555) received a value of -0.661, compared to -0.391 in the original set.
CONCLUSIONS: This study provides an updated EQ-5D-5L value set for China using robust data collection and modelling methods. The new value set reflects more recent societal preferences and corrects limitations of the original study, such as lack of rural representation and quality control. Changes in preferences may be attributed to improved methodology, population diversity, and contextual factors like the COVID-19 pandemic. These findings support regular updates to value sets to maintain their policy relevance and applicability in economic evaluations and health technology assessments in China.
Conference/Value in Health Info
2025-09, ISPOR Real-World Evidence Summit 2025, Tokyo, Japan
Value in Health Regional, Volume 49S (September 2025)
Code
RWD58
Topic Subcategory
Distributed Data & Research Networks
Disease
No Additional Disease & Conditions/Specialized Treatment Areas