Mapping the Chinese Version of the EORTC QLQ-BR53 Onto the EQ-5D-5L and SF-6D Utility Scores
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ORIGINAL RESEARCH ARTICLE
Mapping the Chinese Version of the EORTC QLQ‑BR53 Onto the EQ‑5D‑5L and SF‑6D Utility Scores Tong Liu1,2 · Shunping Li1,2 · Min Wang3 · Qiang Sun1,2 · Gang Chen4
© Springer Nature Switzerland AG 2020
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.
Key Points for Decision Makers
Electronic Supplementary Material The online version of this article (https://doi.org/10.1007/s40271-020-00422-x) contains supplementary material, which is available to authorized users. * Shunping Li [email protected] 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
Qingdao Municipal Hospital, Qingdao 266011, China
4
Flinders Centre for Innovation in Cancer, Flinders University, Adelaide, SA 5042, Australia
The goodness-of-fit performance of mapping onto the Short Form 6D (SF-6D) is significantly better than using the 5-level EQ-5D (EQ-5D-5L), thus the derived SF-6D utilities from mapping functions are more appriopriately for calculating quality-adjusted life year. The response mapping used in this study can largely improve prediction accuracy, especially for SF-6D. I
