Mapping the EQ-5D index from the cystic fibrosis questionnaire-revised using multiple modelling approaches

Background This study was designed to develop a mapping algorithm to estimate EQ-5D utility values from Cystic Fibrosis Questionnaire-Revised (CFQ-R) data. Methods A cross-sectional survey of adults with cystic fibrosis (CF) was conducted in the UK. The survey consisted of the CFQ-R, the EQ-5D and a background questionnaire. Eight regression models, exploring item and domain level predictors, were evaluated using three different modelling approaches: ordinary least squares (OLS), Tobit, and a two-part model (TPM). Predictive performance in each model was assessed by intraclass correlations, information criteria (Bayesian information criteria and Alkaike information criteria), and root mean square error (RMSE). Results The survey was completed by 401 participants. For all modelling approaches the best performing item level model included all items, and the best performing domain level model included the CFQ-R Physical-, Role- and Emotional-functioning, Vitality, Eating Disturbances, Weight, and Digestive Symptoms domains and a selection of squared terms. Overall, the item level TPM, including age and gender covariates performed best within sample validation, but OLS and TPM domain models with squared terms performed best out-of-sample and are recommended for mapping purposes. Conclusions Domain and item level models using all three modelling approaches reached an acceptable degree of predictive performance with domain models performing well in out-of-sample validation. These mapping functions can be applied to CFQ-R datasets to estimate EQ-5D utility values for economic evaluations of interventions for patients with cystic fibrosis. Further research evaluating model performance in an independent sample is encouraged.