Modeling disease progression with longitudinal markers

In this article we propose a Bayesian natural history model for disease progression based on the joint modeling of longitudinal biomarker levels, age at clinical detection of disease, and disease status at diagnosis. We establish a link between the longitudinal responses and the natural history of the disease by using an underlying latent disease process that describes the onset of the disease and models the transition to an advanced stage of the disease as dependent on the biomarker levels. We apply our model to data from the Baltimore Longitudinal Study of Aging on prostate-specific antigen to investigate the natural history of prostate cancer.