Abstract
BackgroundMapping of health-related quality-of-life measures to health utility values can facilitate cost-utility evaluation. Regression-based methods tend to lead to shrinkage of variance. This study aims to map the Medical Outcomes Study HIV Health Survey (MOS-HIV) to EuroQoL 5 Dimensions (EQ-5D-3 L) utility index, and to characterize the performance of three mapping methods, including ordinary least squares (OLS), equi-percentile method (EPM), and a recently proposed method called Mean Rank Method (MRM).MethodsThis is a secondary analysis of data from a randomized HIV treatment trial. Baseline data from 421 participants were used to develop mapping functions. Follow-up data from 236 participants was used to validate the mapping functions.ResultsIn the training dataset, MRM and OLS, but not EPM, reproduced the observed mean utility (0.731). MRM, OLS and EPM under-estimated the standard deviation by 0.3, 26.6 and 1.7%, respectively. MRM had the lowest mean absolute error (0.143) and highest intraclass correlation coefficient (0.723) with the observed utility values, whereas OLS had the lowest mean squared error (0.038) and highest R-squared (0.542). Regressing the MRM- and OLS-mapped utility values upon body mass index and log-viral load gave covariate associations comparable to those estimated from the observed utility data (all P > 0.10). EPM did not achieve this property. Findings from the validation data were similar.ConclusionsFunctions are available for mapping the MOS-HIV to the EQ-5D-3 L utility values. MRM and OLS were comparable in terms of agreement with the observed utility values at the individual level. MRM had better performance at the group level in terms of describing the utility distribution.Trial registrationNCT00988039. Registered 30 September 2009.
Highlights
Mapping of health-related quality-of-life measures to health utility values can facilitate cost-utility evaluation
At weeks 0, 48, 96 and 144, the participants completed the MOS-human immunodeficiency virus (HIV) and EuroQoL 5 Dimensions Questionnaire (EQ-5D)-3 L and had their body mass index (BMI) measured; HIV viral load was assayed in real-time at baseline and retrospectively on stored plasma post-baseline
We explored the relationship between EQ-5D-3 L utility and Physical Health Summary (PHS) and Mental Health Summary (MHS) using linear regression and fractional polynomials (FP)
Summary
Mapping of health-related quality-of-life measures to health utility values can facilitate cost-utility evaluation. Cost-utility analysis is an important part of the rational development of health care policy and evaluation of medical interventions It quantifies the cost required for a gain in quality-adjusted life years (QALY) [1] The quality adjustment factor in the estimation of QALY may be. Clinical studies often employed quality of life measures that are descriptive, in the sense that they indicate better or worse quality of life but they do not provide a utility value that has a quantitative interpretation for adjusting survival duration to QALY. These descriptive measures are often conceptually overlapping with preference-based measures and empirically correlated with the utility values.
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