Purpose: In health-related quality of life (HRQOL) studies, data are often collected on multiple domains for two or more groups of study participants. Quantitative measures of relative importance, which are used to rank order the domains based on their ability to discriminate between groups, are an alternative to multiple tests of significance on the group differences. This study describes relative importance measures based on logistic regression (LR) and multivariate analysis of variance (MANOVA) models.
Methods: Relative importance measures are illustrated using data from the Manitoba Inflammatory Bowel Disease (IBD) Cohort Study. Study participants with self-reported active (n = 244) and inactive (n = 105) disease were compared on 12 HRQOL domains from the Inflammatory Bowel Disease Questionnaire (IBDQ) and Medical Outcomes Study 36-item Short-Form (SF-36) Questionnaire.
Results: All but two relative importance measures ranked the IBDQ bowel symptoms and emotional health domains as most important.
Conclusions: MANOVA-based importance measures are recommended for multivariate normal data and when group covariances are equal, while LR measures are recommended for non-normal data and when the correlations among the domains are small. Relative importance measures can be used in exploratory studies to identify a small set of domains for further research.