Modeling Disease Progression with Longitudinal Markers.
In this paper 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 which 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 the data from the Baltimore Longitudinal Study of Aging on prostate specific antigen (PSA) to investigate the natural history of prostate cancer.
Department of Biostatistics, University of Washington, F-600 Health Sciences Building, Box 357232, Seattle, WA, 98195.
Lurdes Y T
LY
Inoue
Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP 665, Box 19024, Seattle, WA, 98109.
Ruth
R
Etzioni
Mathematical Sciences Department, Loyola College in Maryland, Mathematical Sciences Department, 4501 North Charles Street, Baltimore, MD, 21210 and Gerontology Research Center, National Institute on Aging, 5600 Nathan Shock Drive, Baltimore, MD 21224.
Christopher
C
Morrell
Department of Biostatistics, University of Texas, MD Anderson Cancer Center, Unit 447, Houston, TX, 77030.
Peter
P
Müller
Markov Chain Monte Carlo methods; Natural history model; disease progression; latent variables; longitudinal response; prostate specific antigen