The analysis of repeated-measures data presents challenges to investigators
and is a topic for ongoing discussion in the Archives of
General Psychiatry. Traditional methods of statistical analysis (end-point
analysis and univariate and multivariate repeated-measures analysis of variance
[rANOVA and rMANOVA, respectively]) have known disadvantages. More sophisticated
mixed-effects models provide flexibility, and recently developed software
makes them available to researchers.
To review methods for repeated-measures analysis and discuss advantages
and potential misuses of mixed-effects models. Also, to assess the extent
of the shift from traditional to mixed-effects approaches in published reports
in the Archives of General Psychiatry.
The Archives of General Psychiatry from 1989
through 2001, and the Department of Veterans Affairs Cooperative Study 425.
Studies with a repeated-measures design, at least 2 groups, and a continuous
The first author ranked the studies according to the most advanced statistical
method used in the following order: mixed-effects model, rMANOVA, rANOVA,
and end-point analysis.
The use of mixed-effects models has substantially increased during the
last 10 years. In 2001, 30% of clinical trials reported in the Archives of General Psychiatry used mixed-effects analysis.
Repeated-measures ANOVAs continue to be used widely for the analysis
of repeated-measures data, despite risks to interpretation. Mixed-effects
models use all available data, can properly account for correlation between
repeated measurements on the same subject, have greater flexibility to model
time effects, and can handle missing data more appropriately. Their flexibility
makes them the preferred choice for the analysis of repeated-measures data.