Does using factor analysis to select covariates in mapping algorithms improve predictive accuracy? A case of mapping the core 23-item Women’s Health Questionnaire onto the EQ-5D-5L and SF-6D

Objectives: In addition to theoretical justifications, many statistical methods have been used for selecting covariates to include in algorithms mapping non-utility measures onto utilities. However, it is not clear whether using exploratory factor analysis (EFA) as one such method improves the predictive ability of these algorithms. This question is addressed within the context of mapping a non-utility based outcome, the core 23-item Women’s Health Questionnaire (WHQ-23), onto two utility instruments: EuroQoL 5 Dimensions 5 Level (EQ-5D-5L) and the Short Form 6 Dimensions (SF-6D).