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Activity Number: 194
Type: Contributed
Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308314
Title: A Multivariate Semiparametric Model for Longitudinal Binary Data with Application in Sexually Transmitted Infection Research
Author(s): Zhuokai Li*+ and Hai Liu and Wanzhu Tu
Companies: Indiana University School of Medicine and Indiana University School of Medicine and Indiana University School of Medicine
Keywords: bivariate surfaces ; mixed effects models ; multivariate binary outcomes ; penalized likelihood ; splines ; resampling
Abstract:

Repeatedly measured data of multiple binary outcomes are abundant in clinical research. For example, acquisitions of sexually transmitted infections (STIs) with different organisms are often assessed prospectively during follow-up. In this paper, we propose a multivariate semiparametric model for binary infection outcomes and use bivariate thin plate spline surfaces to depict the joint effects of two independent variables. Using the proposed model, we estimate organism-specific infection incidence as a function of age and the number of sexual partners. The model accounts for the interdependency across the outcomes within the same person as well as the serial dependency among repeated measurements. Parameters are estimated by using penalized likelihood method. For inference, we develop a likelihood-based resampling procedure to compare the bivariate effect surfaces across multiple outcomes. A simulation study is conducted to evaluate model performance. As an illustration, we analyzed data collected from a longitudinal observational study of STIs in young women to examine the concurrent influences of age and the number of sexual partners on the infection risks for three organisms.


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