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Activity Number: 75 - Contributed Poster Presentations: Biometrics Section
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #313596
Title: Modeling Simultaneous Responses with Nested Working Correlation and Bayesian Estimates for Longitudinal Data with Time-Dependent Covariates
Author(s): Elsa Aimara Vazquez Arreola* and Jeffrey R Wilson
Companies: Arizona State University and Arizona State University
Keywords: multivariate binary outcomes; within outcome correlation; between outcome correlation

In the analysis of longitudinal data, it is common to characterize the relationship between (repeated) response measures and the covariates. However, when the covariates do vary over time (time-dependent covariates) there is extra relation due to the delayed effects that need to be accounted for. Moreover, it is not uncommon that these studies often consist of simultaneous responses on the subject. However, a joint likelihood function of the simultaneous responses is impossible to afford maximum likelihood estimates as the observations are correlated. We present a simultaneous modeling approach of multiple responses with a hierarchical working correlation matrix while using Bayesian regression estimates on the partitioning of the data matrix. We conduct a simulation study demonstrating the benefits of this model. We demonstrate its fit using studies with a single response and studies with simultaneous responses. We provide code in R and a SAS macro while revisiting two numerical examples, Chinese Quality of Health survey data and Add Heath survey data.

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

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