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|Title:||Mapping between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and five multi-attribute utility instruments (MAUIs)|
|Citation:||PharmacoEconomics, 2017; 35(1):111-124|
|Publisher:||Springer International Publishing|
|Billingsley Kaambwa, Gang Chen, Julie Ratcliffe, Angelo Iezzi, Aimee Maxwell, Jeff Richardson|
|Abstract:||Purpose: Economic evaluation of health services commonly requires information regarding health-state utilities. Sometimes this information is not available but non-utility measures of quality of life may have been collected from which the required utilities can be estimated. This paper examines the possibility of mapping a non-utility-based outcome, the Sydney Asthma Quality of Life Questionnaire (AQLQ-S), onto five multi-attribute utility instruments: Assessment of Quality of Life 8 Dimensions (AQoL-8D), EuroQoL 5 Dimensions 5-Level (EQ-5D-5L), Health Utilities Index Mark 3 (HUI3), 15 Dimensions (15D), and the Short-Form 6 Dimensions (SF-6D). Methods: Data for 856 individuals with asthma were obtained from a large Multi-Instrument Comparison (MIC) survey. Four statistical techniques were employed to estimate utilities from the AQLQ-S. The predictive accuracy of 180 regression models was assessed using six criteria: mean absolute error (MAE), root mean squared error (RMSE), correlation, distribution of predicted utilities, distribution of residuals, and proportion of predictions with absolute errors \0.0.5. Validation of initial ‘primary’ models was carried out on a random sample of the MIC data. Results: Best results were obtained with non-linear models that included a quadratic term for the AQLQ-S score along with demographic variables. The four statistical techniques predicted models that performed differently when assessed by the six criteria; however, the best results, for both the estimation and validation samples, were obtained using a generalised linear model (GLM estimator). Conclusions: It is possible to predict valid utilities from the AQLQ-S using regression methods. We recommend GLM models for this exercise.|
|Description:||Published online: 24 August 2016|
|Rights:||© Springer International Publishing Switzerland 2016|
|Appears in Collections:||Public Health publications|
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