Mapping the HAQ-DI to the EQ-5D-5L and SF-6Dv2 Among Chinese Patients With Gout
Author(s)
Chang Luo, MS1, Tianqi Hong, PhD2, Shitong Xie, PhD1, Jing Wu, PhD1.
1School of Pharmaceutical Science and Technology, Faculty of Medicine, Tianjin University, Tianjin, China, 2School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada.
1School of Pharmaceutical Science and Technology, Faculty of Medicine, Tianjin University, Tianjin, China, 2School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada.
Presentation Documents
OBJECTIVES: This study aimed to develop mapping algorithms to predict EQ-5D-5L and SF-6Dv2 utility values from health assessment questionnaire-disability index (HAQ-DI) scores in gout patients in China.
METHODS: Respondents recruited from the representative regions of China completed an online survey and the sample was randomly divided into development (80%) and validation (20%) datasets. Spearman’s correlation analyses were conducted to assess the conceptual overlap for HAQ-DI with the EQ-5D-5L and SF-6Dv2. Seven models, including OLS, Tobit, CLAD, GLM, TPM, ALDVMM, BM, and five predictor sets were explored to estimate mapping algorithms using the development dataset. Model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), Akaike information criterion (AIC), Bayesian information criterion (BIC), and intraclass correlation coefficient (ICC).
RESULTS: A total of 1,000 patients with gout (69.7% male, mean [SD] age 54.5 [13.4] years) were included in this study. The average score (SD) of HAQ-DI was 0.742 (0.583) and the mean utility value (SD) of EQ-5D-5L and SF-6Dv2 was 0.772(0.189) and 0.658 (0.156), respectively. The Spearman’s correlation coefficients for HAQ-DI score with EQ-5D-5L and SF-6Dv2 utilities were 0.725 and 0.571, respectively. The best-performing models predicting EQ-5D-5L and SF-6Dv2 utilities both used the HAQ-DI subscale score from stepwise regression as a predictor in the OLS model (MAE: 0.095 and 0.096 for the EQ-5D-5L and SF-6Dv2; RMSE: 0.131 and 0.125 for the EQ-5D-5L and SF-6Dv2; AIC: -960.559 for the EQ-5D-5L and -1063.409 for the SF-6Dv2; BIC: -927.767 for the EQ-5D-5L and -1039.986 for the SF-6Dv2; ICC: 0.689 and 0.514 for the EQ-5D-5L and SF-6Dv2).
CONCLUSIONS: This study provides a mapping framework to estimate EQ-5D-5L and SF-6Dv2 utility values from HAQ-DI scores. OLS models with the HAQ-DI subscale score from stepwise regression can be used to predict both the EQ-5D-5L and SF-6Dv2 utility values among patients with gout in China.
METHODS: Respondents recruited from the representative regions of China completed an online survey and the sample was randomly divided into development (80%) and validation (20%) datasets. Spearman’s correlation analyses were conducted to assess the conceptual overlap for HAQ-DI with the EQ-5D-5L and SF-6Dv2. Seven models, including OLS, Tobit, CLAD, GLM, TPM, ALDVMM, BM, and five predictor sets were explored to estimate mapping algorithms using the development dataset. Model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), Akaike information criterion (AIC), Bayesian information criterion (BIC), and intraclass correlation coefficient (ICC).
RESULTS: A total of 1,000 patients with gout (69.7% male, mean [SD] age 54.5 [13.4] years) were included in this study. The average score (SD) of HAQ-DI was 0.742 (0.583) and the mean utility value (SD) of EQ-5D-5L and SF-6Dv2 was 0.772(0.189) and 0.658 (0.156), respectively. The Spearman’s correlation coefficients for HAQ-DI score with EQ-5D-5L and SF-6Dv2 utilities were 0.725 and 0.571, respectively. The best-performing models predicting EQ-5D-5L and SF-6Dv2 utilities both used the HAQ-DI subscale score from stepwise regression as a predictor in the OLS model (MAE: 0.095 and 0.096 for the EQ-5D-5L and SF-6Dv2; RMSE: 0.131 and 0.125 for the EQ-5D-5L and SF-6Dv2; AIC: -960.559 for the EQ-5D-5L and -1063.409 for the SF-6Dv2; BIC: -927.767 for the EQ-5D-5L and -1039.986 for the SF-6Dv2; ICC: 0.689 and 0.514 for the EQ-5D-5L and SF-6Dv2).
CONCLUSIONS: This study provides a mapping framework to estimate EQ-5D-5L and SF-6Dv2 utility values from HAQ-DI scores. OLS models with the HAQ-DI subscale score from stepwise regression can be used to predict both the EQ-5D-5L and SF-6Dv2 utility values among patients with gout in China.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
PCR58
Topic
Patient-Centered Research
Topic Subcategory
Health State Utilities, Patient-reported Outcomes & Quality of Life Outcomes
Disease
SDC: Musculoskeletal Disorders (Arthritis, Bone Disorders, Osteoporosis, Other Musculoskeletal)