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Activity Number: 475
Type: Topic Contributed
Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #316221
Title: Modeling Multivariate Conditional Distributions Using Copula for Longitudinal Data
Author(s): Xin Tian* and Colin O. Wu
Companies: National Heart, Lung, and Blood Institute and NIH
Keywords: longitudinal analysis ; multivariate distribution ; conditional distribution ; multivariate copula ; ranks
Abstract:

In longitudinal medical studies, it is important to jointly track multivariate risk factors and understand their interdependence structure. We propose to use multivariate copula models to link individual marginal outcome variables, where each marginal distribution may change with time and other covariates. Different copula models are explored and compared by goodness-of-fit tests. An application to a large epidemiological study of childhood cardiovascular risk factors is presented using the proposed approach with multivariate copula.

Authors who are presenting talks have a * after their name.

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