Comparing EQ-5D Scores for Comorbid Health Conditions Estimated Using 5 Different Methods

Background: There is currently no consensus on the most appropriate method to estimate health state utility values (HSUVs) for comorbid health conditions. Objective: The objective of the study was to assess the accuracy by applying 5 different methods to an EQ-5D dataset. Methods: EQ-5D data (n = 41,174) from the Health Survey for England were used to compare HSUVs generated using the additive, multiplicative and minimum methods, the adjusted decrement estimator, and a linear regression. Results: The additive and multiplicative methods underestimated the majority of HSUVs and the magnitude of the errors increased as the actual HSUV increased. Conversely, the minimum and adjusted decrement estimator methods overestimated the majority of HSUVs and the magnitude of errors increased as the actual HSUV decreased. Although the simple linear model produced the most accurate results, there was a tendency to underpredict higher HSUVs and overpredict lower HSUVs. The magnitude and direction of mean errors could be driven by the actual scores being estimated in addition to the technique used and the HSUVs estimated using an adjusted baseline were generally more accurate. Conclusions: The additive and minimum methods performed very poorly in our data. Although the simple linear model gave the most accurate results, the model requires validating in external data obtained from the EQ-5D and other preference-based measures. Based on the current evidence base, we would recommend the multiplicative method is used together with a range of univariate sensitivity analyses.