Background: EQ-5D-3L valuation studies continue to employ the MVH protocol or variants of MVH. One issue that has received attention is the selection of the states for direct valuation by respondents. Changes in the valuation subset have been found to change the coefficients of the utility function. The purpose of this study was to test the performance of valuation subsets based on orthogonal experiment designs. The design of the study also allowed a comparison of models based on raw or untransformed VAS values with values transformed at the level of the respondent and at the aggregate level. Methods: Two different valuation subsets were developed based on orthogonal arrays. A VAS elicitation was undertaken with two groups of similar respondents and the resulting utility functions based on the valuations of the two different valuation subsets were compared using mean absolute errors between model and observed values, and by correlation with values in and out of sample. The impact of using untransformed versus VAS values transformed at the level of the individual and at aggregate level and the inclusion of a constant term in the utility functions were also investigated. Results: The utility functions obtained from the two valuation subsets were very similar. The models that included a constant and based on raw VAS values from the two valuation studies returned rank correlation coefficients of 0.994 and 0.995 when compared with respective observed values. MAEs of model values with observed values were 2.4% or lower for all models that included a constant term. Several models were developed and evaluated for the combined data (from both valuation subsets). The model that included the N3 term performed best. Conclusions: The finding that two very different valuation subsets can produce strikingly similar utility functions suggests that orthogonal designs should be given some attention in further studies. The impact of rescaling VAS values at the level of the individual versus at aggregate level had minimal impact on the performance of the models when compared to models based on the raw VAS values.