TY - JOUR
T1 - Modeling disease progression with longitudinal markers
AU - Inoue, Lurdes Y.T.
AU - Etzioni, Ruth
AU - Morrell, Christopher
AU - Müller, Peter
N1 - Funding Information:
Lurdes Y. T. Inoue is Assistant Professor, Department of Biostatistics, University of Washington, Seattle, WA 98195 (E-mail: linoue@u.washington.edu). Ruth Etzioni is Full Member, Fred Hutchinson Cancer Research Center, Seattle, WA 98109. Christopher Morrell is Professor, Mathematical Sciences Department, Loyola College in Maryland, Baltimore, MD 21210, and Gerontology Research Center, National Institute on Aging, Baltimore, MD 21224. Peter Müller is Professor, Department of Biostatistics, University of Texas, MD Anderson Cancer Center, Houston, TX 77030. This work was supported in part by grants 5 U01 CA 88160 and R01 CA 100778 from the National Cancer Institutes. L. Inoue also acknowledges partial support from Career Development Funding from the Department of Biostatistics, University of Washington. The authors thank the editor, associate editor, and reviewers for their suggestions.
PY - 2008/3
Y1 - 2008/3
N2 - 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.
AB - 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.
KW - Disease progression
KW - Latent variables
KW - Longitudinal response
KW - Markov chain Monte Carlo methods
KW - Natural history model
KW - Prostate-specific antigen
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U2 - 10.1198/016214507000000356
DO - 10.1198/016214507000000356
M3 - Article
AN - SCOPUS:42349097208
SN - 0162-1459
VL - 103
SP - 259
EP - 270
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 481
ER -