Applications of global statistics in analysing quality of life data

Quality of life (QOL) instruments usually consist of a number of components, each of which deals specifically with a particular functionally related dysfunction. In a clinical trial whose primary aim is the evaluation of the treatment by means of QOL instruments, analysis of each of the components usually consists of either univariate analysis of variance (ANOVA) or some non-parametric methods. This multiple testing approach can produce an increase in false positive findings. One attempt to correct for this is the Bonferroni adjustment. Another approach is to apply global statistics (parametric or non-parametric) for the null hypothesis of no treatment difference versus the alternative hypothesis that one treatment is uniformly better than the other for QOL instruments as a whole. Data from a randomized double-blind trial of 111 congestive heart failure patients, which involved four QOL instruments, were analysed with univariate ANOVA, Bonferroni adjustment, parametric and non-parametric global statistics. The global statistics complemented the univariate methods and made the presentation of QOL data very effective. I recommend the general use of global statistics in analysis of QOL data.