Abstract #300118

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JSM 2003 Abstract #300118
Activity Number: 336
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #300118
Title: Using Multivariate Mixed-Effects Models to Predict Hypertension
Author(s): Christopher H. Morrell*+ and Larry J. Brant and Shan Sheng and Samer Najjar
Companies: Loyola College in Maryland and Gerontology Research Center and National Institute of Aging and National Institute on Aging
Address: Mathematical Sciences Dept., Baltimore, MD, 21210-2601,
Keywords: classification ; disease screening ; longitudinal data
Abstract:

Using several variables known to be related to blood pressure, a multivariate classification method is developed to predict the onset of clinical hypertension. We use a multivariate mixed-effects model to describe longitudinal changes in systolic blood pressure, triglycerides, and body mass index before any evidence of clinical hypertension. The patterns of change in the three variables are allowed to vary depending on whether the subject developed hypertension or not. Empirical Bayes estimation is used to obtain posterior probabilities that are used to predict whether an individual will develop hypertension or not. The rule is applied sequentially one multivariate observation at a time until the subject is classified as having hypertension or until the last observation has been used. The analyses are performed for all three variables as well as for each pair of two of the three variables and for each variable separately. The correct classification rates are compared among the various analyses.

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