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Utilities Derived from Visual Analog Scale Scores in Patients with HIV/AIDS

Background. Visual analog scale (VAS) scores are used as global quality-of-life indicators and, unlike true utilities (which assess the desirability of health states v. an external metric), are often collected in HIV-related clinical trials. The purpose of this study was to derive and evaluate transformations relating aggregate VAS scores to utilities for current health in patients with HIV/AIDS. Methods. HIV-specific transformations were developed using linear and nonlinear regression to attain models that best fit mean VAS and standard gamble (SG) utility values directly derived from 299 patients with HIV/AIDS participating in a multicenter study of health values. The authors evaluated the transformations using VAS and SG utility values derived directly from patients in other HIV/AIDS studies. Derived transformations were also compared with published transformations. Results. A simple linear transformation was derived (u = 0.44v + 0.49), as was the exponent for a curvilinear model (u = 1-[1- v] 1.6 ), where u =the sample mean utility and v the sample mean VAS score. The curvilinear transformation predicted values within 0.10 of the actual SG utility in 5 of 8 estimates and within 0.05 in 3 of 8 estimates (absolute error ranged from -0.01 to +0.21). The linear transformation performed some-what better, predicting within 0.10 of the actual SG value in 6 of 8 cases and within 0.05 in 5 of 8 estimates (absolute error ranged from -0.05 to +0.13). An alternative linear model (u =v +0.018) derived from the literature performed similarly to our linear model (7 of 8 predictions within 0.10, 1 of 8 estimates within 0.05, and absolute error ranging from -0.15 to +0.10), whereas an alternative published curvilinear model (u =1 - [1 - v] 2.3 ) performed the least well (2 of 8 estimates within 0.10 of the actual values and no estimates within 0.05). Conclusions. Predicted utilities are a reasonable alter-native for use in HIV/AIDS decision analyses and costeffectiveness analyses. Linear transformations performed better than curvilinear transformations in this context and can be used to convert aggregate VAS scores to aggregate SG values in large HIV/AIDS studies that collect VAS data but not utilities.