Tong Liu1,2, Shunping Li3,4, Min Wang5, Qiang Sun1,2, Gang Chen6. 1. School of Health Care Management, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China. 2. NHC Key Laboratory of Health Economics and Policy Research (Shandong University), Jinan, 250012, China. 3. School of Health Care Management, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China. lishunping@sdu.edu.cn. 4. NHC Key Laboratory of Health Economics and Policy Research (Shandong University), Jinan, 250012, China. lishunping@sdu.edu.cn. 5. Qingdao Municipal Hospital, Qingdao, 266011, China. 6. Flinders Centre for Innovation in Cancer, Flinders University, Adelaide, SA, 5042, Australia.
Abstract
OBJECTIVE: This study aimed to develop mapping algorithms from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-BR53, including EORTC QLQ-C30 and QLQ-BR23) onto the 5-level EQ-5D (EQ-5D-5L) and Short Form 6D (SF-6D) utility scores. METHODS: The data were taken from 607 breast cancer patients in mainland China. The EQ-5D-5L and SF-6D instruments were scored using Chinese-specific tariffs. Three model specifications and seven statistical techniques were used to derive mapping algorithms, including ordinary least squares (OLS), Tobit, censored least absolute deviation (CLAD) model, generalized linear model (GLM), robust MM-estimator, finite mixtures of beta regression model for directly estimating health utility, and using ordered logit regression (OLOGIT) to predict response levels. A five-fold cross-validation approach was conducted to test the generalizability of each model. Two key goodness-of-fit statistics (mean absolute error and mean squared error) and three secondary statistics were employed to choose the optimal models. RESULTS: Participants had a mean ± standard deviation (SD) age of 49.0 ± 9.8 years. The mean ± SD health state utility scores were 0.828 ± 0.184 (EQ-5D-5L) and 0.646 ± 0.125 (SF-6D). Mapping performance was better when both the QLQ-C30 and QLQ-BR23 dimensions were considered rather than when either of these dimensions were used alone. The mapping functions from the optimal direct mapping and indirect mapping approaches were reported. CONCLUSIONS: The algorithms reported in this paper enable EORTC QLQ-BR53 breast cancer data to be mapped into utilities predicted from the EQ-5D-5L and SF-6D. The algorithms allow for the calculation of quality-adjusted life years for use in breast cancer cost-effectiveness analyses studies.
OBJECTIVE: This study aimed to develop mapping algorithms from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-BR53, including EORTC QLQ-C30 and QLQ-BR23) onto the 5-level EQ-5D (EQ-5D-5L) and Short Form 6D (SF-6D) utility scores. METHODS: The data were taken from 607 breast cancerpatients in mainland China. The EQ-5D-5L and SF-6D instruments were scored using Chinese-specific tariffs. Three model specifications and seven statistical techniques were used to derive mapping algorithms, including ordinary least squares (OLS), Tobit, censored least absolute deviation (CLAD) model, generalized linear model (GLM), robust MM-estimator, finite mixtures of beta regression model for directly estimating health utility, and using ordered logit regression (OLOGIT) to predict response levels. A five-fold cross-validation approach was conducted to test the generalizability of each model. Two key goodness-of-fit statistics (mean absolute error and mean squared error) and three secondary statistics were employed to choose the optimal models. RESULTS:Participants had a mean ± standard deviation (SD) age of 49.0 ± 9.8 years. The mean ± SD health state utility scores were 0.828 ± 0.184 (EQ-5D-5L) and 0.646 ± 0.125 (SF-6D). Mapping performance was better when both the QLQ-C30 and QLQ-BR23 dimensions were considered rather than when either of these dimensions were used alone. The mapping functions from the optimal direct mapping and indirect mapping approaches were reported. CONCLUSIONS: The algorithms reported in this paper enable EORTC QLQ-BR53 breast cancer data to be mapped into utilities predicted from the EQ-5D-5L and SF-6D. The algorithms allow for the calculation of quality-adjusted life years for use in breast cancer cost-effectiveness analyses studies.
Authors: C C Earle; R H Chapman; C S Baker; C M Bell; P W Stone; E A Sandberg; P J Neumann Journal: J Clin Oncol Date: 2000-09-15 Impact factor: 44.544
Authors: Allan J Wailoo; Monica Hernandez-Alava; Andrea Manca; Aurelio Mejia; Joshua Ray; Bruce Crawford; Marc Botteman; Jan Busschbach Journal: Value Health Date: 2017-01 Impact factor: 5.725