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Abstract Details
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Activity Number:
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27
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #302659 |
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Title:
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A Mixed-Effects Model for Multivariate Longitudinal Data Analysis
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Author(s):
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Walter Faig*+
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Companies:
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University of California at San Diego
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Address:
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3775 D Miramar St., La Jolla, CA, 92037,
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Keywords:
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Multivariate Longitudinal Data ;
EM Algorithm
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Abstract:
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We propose a scaled linear mixed effects model for the analysis of multivariate longitudinal data. This type of data occurs frequently in medical studies, such as aging, HIV AIDS, or reproductive medicine, where researchers are interested in treatment effects on several observables. The existing analysis of these types of data often look at each univariate outcome separately over time, or at the multivariate outcome at a single time point. Under our joint modeling we propose a set of estimating equations and an EM type algorithm to estimate the parameters of our model. An application of our model is provided using pregnancy outcome data, and we examine its success via the asymptotic behavior of our parameter estimate and finite sample simulation studies.
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