Estimating prediction equations in repeated measures designs.

Abstract

Experimental designs with repeated measures allow response patterns over time (or dose) to be modelled and compared between different homogeneous groups. Issues in data analysis often focus on the pattern of variation of the repeated measures, the appropriateness of a univariate or multivariate analysis, and the shape of the response pattern. An aspect of analysis that is often of equal importance is the development of a regression model for response once the pattern has been characterized. Analysis of variance or multivariate growth curve results often do not include easily interpretable regression equation estimates that can be used for prediction. We present methods and tables that permit simple construction of such predictive equations for repeated measures designs when response is modelled as a polynomial over time with univariate or multivariate analyses.