An exploratory analysis of interaction effects in EQ-5D valuation using saturated EQ VAS datasets from the Netherlands, the United Kingdom, and China

OBJECTIVES: To examine whether interaction terms improve predictive accuracy in EQ-5D valuation models using fully saturated datasets, and to identify parsimonious interaction specifications that balance model performance and feasibility. METHODS: A secondary analysis of three saturated EQ-5D visual analogue scale (VAS) datasets was conducted: EQ-5D-3 L from the Netherlands and the United Kingdom, and EQ-5D-5 L from China. The analysis followed three steps. First, main-effects models were estimated as a reference and compared with models including all second-order interaction terms. Second, the contribution of interaction terms was assessed by examining how model performance metrics (RMSE, R², AIC, and BIC) changed with the number of interaction terms included, and by ranking individual interaction terms according to their impact on RMSE across datasets. Third, parsimonious models using summary interaction terms were evaluated to capture key non-additive effects. RESULTS: Including interaction terms consistently improved predictive performance across all three datasets, although absolute improvements were modest. Interaction effects were strongest for combinations of severe health problems, and all interaction coefficients were positive, consistent with diminishing marginal utility. Interactions involving pain/discomfort and anxiety/depression were among the most influential across datasets. Parsimonious specifications incorporating summary interaction terms-N3D1 for EQ-5D-3 L and N45D1 for EQ-5D-5 L-captured much of the performance gain achieved by fully interacted models. CONCLUSIONS: Interaction effects are empirically detectable and systematically improve model performance in EQ-5D valuation when saturated data are available. Summary interaction terms provide a practical approach to capturing key non-additive effects while maintaining model parsimony.