The idea behind repeated measures is that we measure the response variable over time or under different conditions.
- If A and B are somehow the same, for example measurements of the same variable or of comparable variables with the same units, then you may choose to conduct a repeated measures analysis. For example, you may do the analysis on left arm and right arm of subjects.
- If A and B are fundamentally different things, then you may choose not to use repeated measures analysis. For example, If A were age and B were weight of some subjects, and they are of very different scales, then you may decide fitrm is not appropriate.
As a workaround, it would be possible to model A and B separately. An advantage is that they would not be constrained to have the same functional relationship. For example, A could be a linear function of X and B could be a quadratic function of X. It would also be possible to study the residuals from the two models in order to decide the degree of correlation between A and B after adjusting for the between-subject models.
