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 somewhat 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 alternative for use in HIV/AIDS decision analyses and cost-effectiveness 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.