MULTI-LEVEL MODELS FOR REPEATED MEASUREMENT DATA: APPLICATION TO QUALITY OF LIFE DATA IN CLINICAL TRIALS

✍ Scribed by HEATHER J. BEACON; SIMON G. THOMPSON

• Publisher: John Wiley and Sons
• Year: 1996
• Tongue: English
• Weight: 985 KB
• Volume: 15
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(19961230)15:24<2717::aid-sim518>3.0.co;2-e

✦ Synopsis

Quality of life data present considerable statistical challenges because of their longitudinal and multi- dimensional 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.