The EQ-5D, a widely used multiattribute utility instrument, is commonly used in health economic evaluations where the goal is to decide on which treatments to reimburse. Like other instruments, value sets of the EQ-5D are constructed using valuation studies typically valuing a subset of the health states and using predicted values from a regression model for the unvalued health states. In current practice the prediction errors associated with the value sets are substantial. The goal of this work is 2-fold. First, derive a formula of the mean squared error (MSE) of a value set assuming that the value set is estimated using a linear mixed model with either an independent or a Gaussian spatial correlation on the model misspecification error. Second, explore the effect of the number of health states directly valued, the number of participants and the correlation structure on the MSE. Keeping the total number of participants and the total number of valuations fixed, valuing all 242 health states of the EQ-5D-3L was found to reduce the MSE considerably compared with the common practice of valuing only 42 health states. Furthermore, an independent correlation structure with 3773 participants valuing 42 health states produced the MSE that can be achieved with less than 600 participants valuing all 242 health states under a Gaussian spatial correlation structure. Based on the comparison of the MSE values of some of the well-known designs our suggestion is to value more health states and to use a model with spatially correlated misspecification errors.
Keywords: EQ-5D; mean squared error; model misspecification; sample size; spatial correlation; valuation study.
© 2020 John Wiley & Sons, Ltd.