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.