The validation of published utility mapping algorithms: An example of EORTC QLQ-C30 and EQ-5D in non-small cell lung cancer

Abstract

Background: Mapping algorithms can be used to generate health state utilities when a preference-based instrument is not included in a clinical study. Our aim was to investigate the external validity of published mapping algorithms in non-small cell lung cancer (NSCLC) between the EORTC QLQ-C30 and EQ-5D instruments and to propose methodology for validating any mapping algorithms. Methods: We conducted a targeted literature review to identify published mappings, then applied these to data from the osimertinib clinical trial programme. Performance of the algorithms was evaluated using the mean absolute error, root mean squared error, and graphical techniques for the observed versus predicted EQ-5D utilities. These statistics were also calculated across the range of utility values (as well as ordinary least squares and quantile regression), to investigate how the mappings fitted across all values, not simply around the mean utility. Results: Three algorithms developed in NSCLC were identified. The algorithm based on response mapping (Young et al., 2015) fitted the validation dataset across the range of observed values with similar fit statistics to the original publication (overall MAE of 0.087 vs 0.134). The two algorithms based on beta-binomial models presented a poor fit to both the mean and distribution of utility values (MAE 0.176, 0.178). Conclusions: The validation of mapping algorithms is key to demonstrating their generalisability beyond the original dataset, particularly across the range of plausible utility values (not just the mean) - perceived patient similarity being insufficient. The identified algorithm from Young et al. performed well across the range of EORTC scores observed, and thus appears most suitable for use in other studies of NSCLC patients.