Nan Luo1, Gordon Liu2, Minghui Li3, Haijing Guan4, Xuejing Jin5, Kim Rand-Hendriksen6. 1. Saw Swee Hock School of Public Health, National University of Singapore, Singapore. 2. China Center for Health Economic Research, Peking University, Beijing, China; National School of Development, Peking University, Beijing, China. Electronic address: gordonliu@nsd.pku.edu.cn. 3. Department of Clinical Pharmacy and Outcomes Sciences, University of South Carolina, Columbia, SC, USA. 4. China Center for Health Economic Research, Peking University, Beijing, China. 5. China Center for Health Economic Research, Peking University, Beijing, China; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. 6. Health Services Research Centre, Akershus University Hospital, Lørenskog, Norway; Department of Health Management and Health Economics, University of Oslo, Oslo, Norway.
Abstract
OBJECTIVES: To estimate a five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) value set for China using the health preferences of residents living in the urban areas of the country. METHODS: The values of a subset of the EQ-5D-5L-defined health states (n = 86) were elicited using the time trade-off (TTO) technique from a sample of urban residents (n = 1271) recruited from five Chinese cities. In computer-assisted personal interviews, participants each completed 10 TTO tasks. Two additive and two multiplicative regression models were evaluated for their performance in describing the relationship between TTO values and health state characteristics using a cross-validation approach. Final values were generated using the best-performed model and a rescaling method. RESULTS: The 8- and 9-parameter multiplicative models unanimously outperformed the 20-parameter additive model using a random or fixed intercept in predicting values for out-of-sample health states in the cross-validation analysis and their coefficients were estimated with lower standard errors. The prediction accuracies of the two multiplicative models measured by the mean absolute error and the intraclass correlation coefficient were very similar, thus favoring the more parsimonious model. CONCLUSIONS: The 8-parameter multiplicative model performed the best in the study and therefore was used to generate the EQ-5D-5L value set for China. We recommend using rescaled values whereby 1 represents the value of instrument-defined full health in economic evaluation of health technologies in China whenever the EQ-5D-5L data are available.
OBJECTIVES: To estimate a five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) value set for China using the health preferences of residents living in the urban areas of the country. METHODS: The values of a subset of the EQ-5D-5L-defined health states (n = 86) were elicited using the time trade-off (TTO) technique from a sample of urban residents (n = 1271) recruited from five Chinese cities. In computer-assisted personal interviews, participants each completed 10 TTO tasks. Two additive and two multiplicative regression models were evaluated for their performance in describing the relationship between TTO values and health state characteristics using a cross-validation approach. Final values were generated using the best-performed model and a rescaling method. RESULTS: The 8- and 9-parameter multiplicative models unanimously outperformed the 20-parameter additive model using a random or fixed intercept in predicting values for out-of-sample health states in the cross-validation analysis and their coefficients were estimated with lower standard errors. The prediction accuracies of the two multiplicative models measured by the mean absolute error and the intraclass correlation coefficient were very similar, thus favoring the more parsimonious model. CONCLUSIONS: The 8-parameter multiplicative model performed the best in the study and therefore was used to generate the EQ-5D-5L value set for China. We recommend using rescaled values whereby 1 represents the value of instrument-defined full health in economic evaluation of health technologies in China whenever the EQ-5D-5L data are available.
Authors: Juan Zhu; Xin-Xin Yan; Cheng-Cheng Liu; Hong Wang; Le Wang; Su-Mei Cao; Xian-Zhen Liao; Yun-Feng Xi; Yong Ji; Lin Lei; Hai-Fan Xiao; Hai-Jing Guan; Wen-Qiang Wei; Min Dai; Wanqing Chen; Ju-Fang Shi Journal: Qual Life Res Date: 2020-09-15 Impact factor: 4.147
Authors: Zhuoru Liang; Tiantian Zhang; Tengfei Lin; Lishun Liu; Binyan Wang; Alex Z Fu; Xiaobin Wang; Xiping Xu; Nan Luo; Jie Jiang Journal: Qual Life Res Date: 2019-03-04 Impact factor: 4.147