Classical analytical methods for detecting matching effects on treatment outcome.

This article presents a classical approach for analyzing repeated measures designs with specific application to treatment matching studies. The generic treatment matching hypothesis is formulated under the multivariate general linear model, transforming the dependent variables to account for the repeated measures structure of the data. Issues of primary importance in the use of this approach (such as correcting for inflated Type I error and robustness of statistical tests to parametric assumptions) are discussed. The article concludes with an assessment of the strengths and weaknesses of this approach compared with alternative approaches.