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A Bayesian Approach for Incorporating the EQ-5D Visual Analog Scale When Estimating the Health-Related Quality of
Robin Blythe1, Nicole White1, Sanjeewa Kularatna1
1Australian Centre for Health Services Innovation, Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia.
Objectives:
The EuroQoL 3-level version of EQ-5D and 5-level version of EQ-5D questionnaires are often used to quantify health states. They include ordinal responses across 5 health dimensions (EQ-5D index) and an EQ-visual analog scale (EQ-VAS) overall health rating. We investigated the value of incorporating the EQ-VAS to update health utility estimates using a Bayesian framework.
Methods:
We created a joint bivariate normal EQ-VAS and EQ-5D index utility model and compared this to a univariate normal EQ-5D index utility model. We tested these models for 1026 Sri Lankan patients with chronic kidney disease and 94 Australian patients with wounds. We validated our approach by simulating EQ-VAS and EQ-5D index responses and applying our Bayesian model and then comparing the modeled estimates to our observed data.
Results:
The combined model showed a reduction in estimate uncertainty for all respondents. Compared with the EQ-5D index-only model, the mean utility for Sri Lankan respondents dropped from 0.556 (0.534-0.579) to 0.540 (0.521-0.559) in men and increased from 0.489 (0.461-0.518) to 0.528 (0.506-0.550) in women, with reduced credible interval width by 13% and 23%, respectively. The mean utility in Australian respondents moved from 0.715 (0.633-0.800) to 0.716 (0.652-0.782) in men, and 0.652 (0.581-0.723) to 0.652 (0.593-0.711) in women, with reduced credible interval width by 23% and 17%, respectively. The credible interval width for simulated data also narrowed, ranging from 8.3 to 8.5%.
Conclusions:
Including the EQ-VAS through Bayesian methods can add value by reducing requisite sample sizes and decision uncertainty using small amounts of additional data that is often collected but rarely used.
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