A Quantile regression approach for modelling a Health-Related Quality of Life Measure
Objective. The aim of this study is to propose a new approach for modeling the EQ-5D index and EQ-5D VAS in order to explain the lifestyle determinants effect using the quantile regression analysis. Methods. Data was collected within a cross-sectional study that involved a probabilistic sample of 1,622 adults randomly selected from the population register of two Health Authorities of Bologna in northern Italy. The perceived health status of people was measured using the EQ-5D questionnaire. The Visual Analogue Scale included in the EQ-5D Questionnaire, the EQ-VAS, and the EQ-5D index were used to obtain the synthetic measures of quality of life. To model EQ-VAS Score and EQ-5D index, a quantile regression analysis was employed. Quantile Regression is a way to estimate the conditional quantiles of the VAS Score distribution in a linear model, in order to have a more complete view of possible associations between a measure of Health Related Quality of Life (dependent variable) and socio-demographic and determinants data. This methodological approach was preferred to an OLS regression because of the EQ-VAS Score and EQ-5D index typical distribution. Main Results. The analysis suggested that age, gender, and comorbidity can explain variability in perceived health status measured by the EQ-5D index and the VAS.