Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2026
Valuation of EQ-5D-5L With 2 Bolt-On Items: Further Evaluation of the Scaling Factor Model.
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Yang Zhihao, Rand Kim, Finch Aureliano, Luo Nan
Аннотация
OBJECTIVES: The scaling factor model (SFM) uses parameters of existing EQ-5D value sets to estimate value sets for new EQ-5D descriptive systems expanded with bolt-ons. This study aimed to compare the performance of SFM and the standard modeling approach (ie, "standalone" model) for modeling the general public's preferences for EQ-5D-5L health states expanded with bolt-on.
METHODS: In a composite time trade-off (cTTO) valuation study, we selected EQ-5D-5L and bolt-on health states using an orthogonal array design. We randomized 597 respondents to valuing EQ-5D-5L health states (arm 1), EQ-5D-5L states with vision bolt-on (arm 2), or EQ-5D-5L states with cognition bolt-on (arm 3). We modeled the cTTO data derived from arm 1 and used the estimated coefficients to model the cTTO data derived from arm 2 and arm 3 separately using SFM. Both additive and cross-attribute level effects model specifications were used in the analysis. Using a cross-validation method, we evaluated the predictive accuracy of both SFM and standalone models.
RESULTS: SFM showed better predictive accuracy than standalone model in the cross-validation analysis. For EQ-5D states with vision bolt-on, the mean absolute errors were 0.049 and 0.064 for SFM and 0.183 and 0.085 for standalone model; for cognition, the mean absolute errors were 0.051 and 0.047 for SFM and 0.152 and 0.063 for standalone model.
CONCLUSIONS: This study suggests that the SFM could be a viable approach to utilizing existing EQ-5D value sets to estimate value sets for new, bolt-on enhanced EQ-5D health-state descriptive systems.
METHODS: In a composite time trade-off (cTTO) valuation study, we selected EQ-5D-5L and bolt-on health states using an orthogonal array design. We randomized 597 respondents to valuing EQ-5D-5L health states (arm 1), EQ-5D-5L states with vision bolt-on (arm 2), or EQ-5D-5L states with cognition bolt-on (arm 3). We modeled the cTTO data derived from arm 1 and used the estimated coefficients to model the cTTO data derived from arm 2 and arm 3 separately using SFM. Both additive and cross-attribute level effects model specifications were used in the analysis. Using a cross-validation method, we evaluated the predictive accuracy of both SFM and standalone models.
RESULTS: SFM showed better predictive accuracy than standalone model in the cross-validation analysis. For EQ-5D states with vision bolt-on, the mean absolute errors were 0.049 and 0.064 for SFM and 0.183 and 0.085 for standalone model; for cognition, the mean absolute errors were 0.051 and 0.047 for SFM and 0.152 and 0.063 for standalone model.
CONCLUSIONS: This study suggests that the SFM could be a viable approach to utilizing existing EQ-5D value sets to estimate value sets for new, bolt-on enhanced EQ-5D health-state descriptive systems.