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Biostatistics Advance Access originally published online on April 15, 2009
Biostatistics 2009 10(3):535-549; doi:10.1093/biostatistics/kxp009
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach

Cécile Proust-Lima*

Institut National de la Santé et de la Recherche Médicale U897, Biostatistics Department and Université Victor Segalen Bordeaux 2, Bordeaux, F-33076, France
cecile.proust{at}isped.u-bordeaux2.fr

Jeremy M. G. Taylor

Department of Biostatistics and Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA

* To whom correspondence should be addressed.

Prostate-specific antigen (PSA) is a biomarker routinely and repeatedly measured on prostate cancer patients treated by radiation therapy (RT). It was shown recently that its whole pattern over time rather than just its current level was strongly associated with prostate cancer recurrence. To more accurately guide clinical decision making, monitoring of PSA after RT would be aided by dynamic powerful prognostic tools that incorporate the complete posttreatment PSA evolution. In this work, we propose a dynamic prognostic tool derived from a joint latent class model and provide a measure of variability obtained from the parameters asymptotic distribution. To validate this prognostic tool, we consider predictive accuracy measures and provide an empirical estimate of their variability. We also show how to use them in the longitudinal context to compare the dynamic prognostic tool we developed with a proportional hazard model including either baseline covariates or baseline covariates and the expected level of PSA at the time of prediction in a landmark model. Using data from 3 large cohorts of patients treated after the diagnosis of prostate cancer, we show that the dynamic prognostic tool based on the joint model reduces the error of prediction and offers a powerful tool for individual prediction.

Keywords: Error of prediction; Joint latent class model; Mixed model; Posterior probability; Predictive accuracy; Prostate cancer prognosis

Received March 19, 2008; revised October 3, 2008; revised December 5, 2008; revised January 30, 2009; accepted for publication March 2, 2009.


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