This paper, presented at the Joint Statistical Meetings, Anaheim, California, August 1990, is intended to generate greater interest among biomedical researchers in a class of new statistical methods for the analysis of longitudinal data. These methods provide both a powerful set of tools and a rich conceptual framework for thinking about disease progression and related problems, and they could find application in the study of a wide range of chronic diseases. Some very sophisticated longitudinal modeling techniques are now available, but are not being used. This paper describes several new longitudinal stochastic models, discusses factors that may affect their successful use in the modeling of disease progression, and illustrates one model, the Mixed Markov model, in connection with the progression of Alzheimer's disease.
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