Multi-level models for repeated measurement data: application to quality of life data in clinical trials

Quality of life data present considerable statistical challenges because of their longitudinal and multidimensional nature, and also because the available data are often very unbalanced through missing values. Here we exemplify the potential of multi-level models, that is, hierarchical random coefficient models, for such data. The discussion is developed in the context of analysing the quality of life data from a trial of palliative treatment in non-small-cell lung cancer. Not only do multi-level models provide a flexible modelling framework for the investigation of the underlying behaviour of response, for example, giving simple estimates of treatment effects, but they also permit a description of the differences between subjects and allow the analysis of multi-dimensional outcomes. The assumptions of Normality, homogeneity, and independence of the within- and between-subject variance components can be investigated and the models can be extended to provide explicit modelling of variance heterogeneity. It is concluded that multi-level models, for which software is now available, provide a natural and powerful approach to the analysis of longitudinal data in general, and multi-dimensional quality of life data in particular.