Multilevel models for repeated measurement data
in clinical trials
Simon Thompson
Department of Medical Statistics and Evaluation
Royal Postgraduate Medical School, London
Abstract
Multilevel models, that is hierarchical random coefficient models,
have considerable potential for the analysis of repeated measurement
data in clinical trials. This is exemplified here by the analysis of
quality of life data, which are multivariate and unbalanced. Not only
do multilevel models provide a flexible modelling framework for the
investigation of underlying average behaviour, for example yielding
simple estimates of overall treatment effects, but they also permit a
description of the differences between individuals. The assumptions
of normality, homogeneity, and independence of the within and between
subject variance components can be investigated. Indeed the models
can be used to provide explicit modelling of variance heterogeneity.
An important extension to multilevel models is for the analysis of
binary outcome data. It is concluded that multilevel models, for
which software is now available, provide a natural and powerful
approach to the analysis of longitudinal data in general, and
multidimensional quality of life data in particular.
Reference: Goldstein H. Multilevel statistical models (2nd edition). London:
Edward Arnold,
1995.