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Activity Number: 337 - Approaches for Modeling Clustered and Longitudinal Data
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #313239
Title: Multivariate Generalized Linear Model for Intensive Longitudinal Data with Incorporation of Outcome Variability as a Predictor
Author(s): Maryam Skafyan*
Keywords: Generalized Linear Model; Intensive Longitudinal Data

In classical statistical problems, variability is often considered a nuisance parameter. However, there is evidence that in certain settings, underlying variability in subject measures may also be as important as other covariates in predicting future outcomes of interest. However, there is a lack of study on models which include variation as a predictor in the mean model. In addition, in recent years, there is an increased interest in jointly modeling of the outcomes for intensive longitudinal data because a separated analysis, ignoring the inherent association between the outcomes, can lead to a biased result. Joint modeling has been widely studied for continuous outcomes to understanding both between- and within-subject dynamic changes in modeling multiple outcomes. However, there is not any multivariate longitudinal model set up for count data or using variance as a predictor. This study proposed a multivariate generalized linear model for intensive longitudinal data with incorporation of outcome variability as predictors in the mean models to allow for the detection of associations between within-subject variation and an outcome of interest.

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

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