JSM 2005 - Toronto

Abstract #303257

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Activity Number: 437
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #303257
Title: Using Multivariate Mixed-effects Models to Predict Prostate Cancer
Author(s): Christopher Morrell*+ and Larry J. Brant and Shan Sheng and E. Jeffrey Metter
Companies: Loyola College in Maryland and National Institute on Aging and National Institute on Aging and National Institute on Aging
Address: Mathematical Sciences Department, Baltimore, MD, 21210, United States
Keywords: Classification ; Disease screening ; Longitudinal data
Abstract:

Using several variables known to be related to prostate cancer, a multivariate classification method is developed to predict the onset of clinical prostate cancer. We use a multivariate, mixed-effects model to describe longitudinal changes in prostate-specific antigen (PSA), free testosterone, and body mass index (BMI) before any clinical evidence of prostate cancer. The patterns of change in the three variables are allowed to vary depending on whether the subject developed prostate cancer and the severity of the prostate cancer at diagnosis. Empirical Bayes estimation is used to obtain posterior probabilities used to predict whether an individual will develop prostate cancer and, if so, whether it is a high-risk or low-risk cancer. The rule is applied sequentially one multivariate observation at a time until the subject is classified or until the last observation has been used. The analyses are performed for all three variables separately as well as for each pair and all three variables simultaneously. The classification rates are compared among the various analyses.

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Revised March 2005