Mapping the GIS 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 Gout Impact Scale GIS 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 GIS 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 GIS was 53.763 (13.624) 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 GIS score with EQ-5D-5L and SF-6Dv2 utilities were 0.470 and 0.540, respectively. The best-performing models predicting EQ-5D-5L and SF-6Dv2 utilities were OLS and GLM used the GIS subscale scores as a predictor, respectively (MAE: 0.123 for EQ-5D-5L and 0.096 for SF-6Dv2; RMSE: 0.163 for EQ-5D-5L and 0.129 for SF-6Dv2; AIC: -611.981 for the EQ-5D-5L and -1015.776 for the SF-6Dv2; BIC: -583.873 for the EQ-5D-5L and -987.668 for the SF-6Dv2; ICC: 0.418 for EQ-5D-5L and 0.473 for SF-6Dv2).
CONCLUSIONS: This study provides a mapping framework to estimate EQ-5D-5L and SF-6Dv2 utility values from GIS scores. OLS and GLM models with the GIS subscale score 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 GIS 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 GIS was 53.763 (13.624) 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 GIS score with EQ-5D-5L and SF-6Dv2 utilities were 0.470 and 0.540, respectively. The best-performing models predicting EQ-5D-5L and SF-6Dv2 utilities were OLS and GLM used the GIS subscale scores as a predictor, respectively (MAE: 0.123 for EQ-5D-5L and 0.096 for SF-6Dv2; RMSE: 0.163 for EQ-5D-5L and 0.129 for SF-6Dv2; AIC: -611.981 for the EQ-5D-5L and -1015.776 for the SF-6Dv2; BIC: -583.873 for the EQ-5D-5L and -987.668 for the SF-6Dv2; ICC: 0.418 for EQ-5D-5L and 0.473 for SF-6Dv2).
CONCLUSIONS: This study provides a mapping framework to estimate EQ-5D-5L and SF-6Dv2 utility values from GIS scores. OLS and GLM models with the GIS subscale score 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
PCR7
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)