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Biostatistics (2004), 5, 3, pp. 483-500
Biostatistics Vol. 5 No. 3 © Oxford University Press 2004; all rights reserved.

Combining longitudinal studies of PSA

Lurdes Y. T. Inoue*

University of Washington, Department of Biostatistics, F-600 Health Sciences Building, Campus Mail Stop 357232, Seattle, WA 98195 and Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP 665, P.O. Box 19024, Seattle, WA 98109, USA
linoue{at}u.washington.edu

Ruth Etzioni

Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP 665, P.O. Box 19024, Seattle, WA 98109, USA

Elizabeth H. Slate

Medical University of South Carolina, Department of Biometry and Epidemiology, 135 Cannon Street, Suite 303, P.O. Box 250835, Charleston, SC 29425, USA

Christopher Morrell

Loyola College in Maryland, Mathematical Sciences Department, 4501 North Charles Street, Baltimore, MD 21210, USA

David F. Penson

Keck School of Medicine and University of Southern California/Norris Cancer Center, Department of Urology, 1441 Eastlake Avenue, Suite 7416, Los Angeles, CA 90033, USA

* To whom correspondence should be addressed.

Prostate-specific antigen (PSA) is a biomarker commonly used to screen for prostate cancer. Several studies have examined PSA growth rates prior to prostate cancer diagnosis. However, the resulting estimates are highly variable. In this article we propose a non-linear Bayesian hierarchical model to combine longitudinal data on PSA growth from three different studies. Our model enables novel investigations into patterns of PSA growth that were previously impossible due to sample size limitations. The goals of our analysis are twofold: first, to characterize growth rates of PSA accounting for differences when combining data from different studies; second, to investigate the impact of clinical covariates such as advanced disease and unfavorable histology on PSA growth rates.

Keywords: Bayesian hierarchical model; Interval-censored data; Longitudinal data; Meta-analysis; Prostate-specific antigen (PSA)


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