This paper considers a latent class model to uncover subpopulation structure for both biomarker trajectories and the probability of disease outcome in highly unbalanced longitudinal data. A speci"c pattern of trajectories can be viewed as a latent class in a "nite mixture where membership in latent classes is modelled with a polychotomous logistic regression. The biomarker trajectories within a latent class are described by a linear mixed model with possibly time-dependent covariates and the probabilities of disease outcome are estimated via a class speci"c model. Thus the method characterizes biomarker trajectory patterns to unveil the relationship between trajectories and outcomes of disease. The coe$cients for the model are estimated via a generalized EM (GEM) algorithm, a natural tool to use when latent classes and random coe$cients are present. Standard errors of the coe$cients are calculated using a parametric bootstrap. The model "tting procedure is illustrated with data from the Nutritional Prevention of Cancer trials; we use prostate speci"c antigen (PSA) as the biomarker for prostate cancer and the goal is to examine trajectories of PSA serial readings in individual subjects in connection with incidence of prostate cancer.