The Effect of Health State Sampling Methods on Model Predictions of EQ-5D-5L Values
Small Designs Can Suffice
Objective: The current five-level EQ-5D (EQ-5D-5L) valuation protocol requires the valuation of 86 states. It has been demonstrated that the selection of empirically valued health states affects the extrapolated values in three-level EQ-5D (EQ-3D-3L). In this investigation, we aim to compare the performance of the current EQ-5D-5L valuation design with other designs.
Study Design: 1603 university students participated in a valuation study using a visual analog scale (VAS) to produce values for all EQ-5D-5L states. Different designs were generated to test their prediction accuracy.
Methods: Subsamples of the dataset were used to mimic data obtained from a particular design; the remaining dataset was used as the validation set. In addition to EuroQol Group Valuation Technology (EQ-VT) design, alternative subsamples and designs were created using random, orthogonal, and “optimizing D-efficiency” sampling methods. The root mean squared error (RMSE) was used as the measure of prediction accuracy.
Results: The EuroQol Group Valuation Technology (EQ-VT) design showed an average RMSE of 3.44 on EQ-VAS, for all 3125 health states combined. Notably, a 25-state orthogonal design performed similarly to the EQ-VT design, with a smaller RMSE of 3.40, and was thus the most efficient design. One caveat with respect to the orthogonal design was that it did not predict the mild states well.
Conclusions: Our study supports the EQ-VT design. Smaller designs were identified with similar overall prediction accuracy. It is worth investigating whether issues with misprediction of mild states can be resolved, as the use of smaller size designs would reduce the cost of the valuation of EQ-5D-5L considerably.
|, , ,|
|Value in Health|
|Organisation||Erasmus University Rotterdam|
Yang, Z, Luo, N, Bonsel, G.J, van Busschbach, J.J, & Stolk, E.A. (2018). The Effect of Health State Sampling Methods on Model Predictions of EQ-5D-5L Values. Value in Health. doi:10.1016/j.jval.2018.06.015