A log-linear model for ordinal data to characterize differential change among treatments

We propose a family of log-linear models for ordinal data that contain parameters reflecting change patterns to compare treatments relative to change from baseline. Under the most general model, rates of change can depend not only upon the direction of change, but also upon the level of the baseline classification. We describe methods for selection of a parsimonious model and for tests of hypotheses concerning treatment differences. Interpretation of treatment differences in the follow-up response profiles, within baseline strata, employs the concept of stochastic ordering. Data from two clinical trials illustrate the proposed procedure.