Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 103, Issue 481, Pages 259-270Publisher
AMER STATISTICAL ASSOC
DOI: 10.1198/016214507000000356
Keywords
disease progression; latent variables; longitudinal response; Markov chain Monte Carlo methods; natural history model; prostate-specific antigen
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Funding
- NCI NIH HHS [R01 CA100778, U01 CA088160] Funding Source: Medline
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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.
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