Ralph Crott1, Andrew Briggs. 1. Academic Hospital St Luc, Catholic University of Louvain, 10 Avenue Hippocrate, Brussels, 1200, Belgium. rc653@york.ac.uk
Abstract
BACKGROUND: Although cancer-specific Health-Related Quality of Life (HRQOL) are commonly included in randomized clinical trials or other prospective non-randomized clinical studies, it is rare that preference-based instruments are used that allow the calculation of a utility weight suitable for estimating quality-adjusted life-years gained. OBJECTIVE: To develop a mapping algorithm to transform the EORTC QLQ-C30 questionnaire responses into EQ-5D derived utilities. STUDY DESIGN: Retrospective data analysis of a multicentre, multicountry prospective clinical trial in breast cancer patients. METHODS: Regression analysis of individual pairs of EQ-5D and QLQ-C30 scores. RESULTS: A model that explained 80% of the variance was developed to estimate EQ-5D Utilities from QLQ-C30 scores at individual level. From this reliable group level means and deviations can be derived. CONCLUSIONS: Mapping from QLQ-C30 scores to EQ-5D-derived utilities when only QLQ-C30 data are available has been shown to be possible with good accuracy. Validation of the proposed algorithm in other external clinical datasets should be encouraged.
BACKGROUND: Although cancer-specific Health-Related Quality of Life (HRQOL) are commonly included in randomized clinical trials or other prospective non-randomized clinical studies, it is rare that preference-based instruments are used that allow the calculation of a utility weight suitable for estimating quality-adjusted life-years gained. OBJECTIVE: To develop a mapping algorithm to transform the EORTC QLQ-C30 questionnaire responses into EQ-5D derived utilities. STUDY DESIGN: Retrospective data analysis of a multicentre, multicountry prospective clinical trial in breast cancerpatients. METHODS: Regression analysis of individual pairs of EQ-5D and QLQ-C30 scores. RESULTS: A model that explained 80% of the variance was developed to estimate EQ-5D Utilities from QLQ-C30 scores at individual level. From this reliable group level means and deviations can be derived. CONCLUSIONS: Mapping from QLQ-C30 scores to EQ-5D-derived utilities when only QLQ-C30 data are available has been shown to be possible with good accuracy. Validation of the proposed algorithm in other external clinical datasets should be encouraged.
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