Kailu Wang1, Xiaopeng Guo2,3,4,5, Siyue Yu1, Lu Gao2,3,4,5, Zihao Wang2,3,4,5, Huijuan Zhu3,4,5,6, Bing Xing7,8,9,10,11, Shuyang Zhang12,13, Dong Dong14,15. 1. JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China. 2. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 3. Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 4. China Pituitary Disease Registry Center, Beijing, China. 5. China Pituitary Adenoma Specialist Council, Beijing, China. 6. Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 7. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. xingbingemail@aliyun.com. 8. Key Laboratory of Endocrinology of Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. xingbingemail@aliyun.com. 9. China Pituitary Disease Registry Center, Beijing, China. xingbingemail@aliyun.com. 10. China Pituitary Adenoma Specialist Council, Beijing, China. xingbingemail@aliyun.com. 11. China Alliance of Rare Diseases, Beijing, China. xingbingemail@aliyun.com. 12. China Alliance of Rare Diseases, Beijing, China. shuyangzhang103@nrdrs.org. 13. Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. shuyangzhang103@nrdrs.org. 14. JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China. dongdong@cuhk.edu.hk. 15. Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, China. dongdong@cuhk.edu.hk.
Abstract
OBJECTIVE: This study aimed to develop a mapping function that links the acromegaly quality of life (AcroQoL) questionnaire to EQ-5D-5L to obtain a preference-based utility value to inform economic evaluation. METHODS: A nationwide cross-sectional questionnaire survey among patients with acromegaly was conducted online in China during 17 December 2019 to 6 January 2020. The study sample was randomly divided into a training set and a validation set. Ordinary least squares (OLS), Tobit, beta-based mixture, and adjusted limited dependent variable mixture models were tested for development of the function in the training set. Total and subscale scores and individual items of AcroQoL were included as predictors in the models along with their squared terms and demographic and clinical characteristics, and selected by backward stepwise selection. The root mean square error, mean absolute error, Akaike's information criterion, Bayesian information criterion and adjusted R-square were used to assess goodness of fit and predictive ability of the models. RESULTS: There were 424 adult patients with acromegaly eligible for this analysis. Average EQ-5D-5L index score and AcroQoL score for them was 0.82 (SD = 0.15) and 44.3 (SD = 22.9), respectively. A total of 60 candidate models were tested. Considering model simplicity and predictive ability in both training and validation set, the best model was the OLS model using scores of physical dimension and its square term as predictors. CONCLUSION: A validated mapping function was developed in this study for estimating EQ-5D scores using AcroQoL outcomes. Its external validity can be further tested in other population with Acromegaly.
OBJECTIVE: This study aimed to develop a mapping function that links the acromegaly quality of life (AcroQoL) questionnaire to EQ-5D-5L to obtain a preference-based utility value to inform economic evaluation. METHODS: A nationwide cross-sectional questionnaire survey among patients with acromegaly was conducted online in China during 17 December 2019 to 6 January 2020. The study sample was randomly divided into a training set and a validation set. Ordinary least squares (OLS), Tobit, beta-based mixture, and adjusted limited dependent variable mixture models were tested for development of the function in the training set. Total and subscale scores and individual items of AcroQoL were included as predictors in the models along with their squared terms and demographic and clinical characteristics, and selected by backward stepwise selection. The root mean square error, mean absolute error, Akaike's information criterion, Bayesian information criterion and adjusted R-square were used to assess goodness of fit and predictive ability of the models. RESULTS: There were 424 adult patients with acromegaly eligible for this analysis. Average EQ-5D-5L index score and AcroQoL score for them was 0.82 (SD = 0.15) and 44.3 (SD = 22.9), respectively. A total of 60 candidate models were tested. Considering model simplicity and predictive ability in both training and validation set, the best model was the OLS model using scores of physical dimension and its square term as predictors. CONCLUSION: A validated mapping function was developed in this study for estimating EQ-5D scores using AcroQoL outcomes. Its external validity can be further tested in other population with Acromegaly.