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Activity Number: 257 - SPEED: Longitudinal/Correlated Data
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 2:45 PM
Sponsor: Biometrics Section
Abstract #332919
Title: The Implementation of Moderated T-Tests in Linear Mixed-Effects Models
Author(s): Lianbo Yu* and Jianying Zhang and Guy Brock and Soledad Fernandez
Companies: Ohio State University and Ohio State University and Ohio State University College of Medicine and The Ohio State University
Keywords: Moderated T-test; Variance Smoothing; Linear Mixed-effects Model; Hierarchical Bayesian Model

Gene expression profiling experiments with few replications lead to great variability in the estimates of gene variances, therefore these are not robust estimates. Toward this end, several moderated t-test methods have been developed to reduce this variability and to increase power of the tests. All these methods assume a linear fixed-effects model and residual variances are smoothed under a hierarchical Bayesian framework. In this talk, we will demonstrate the implementation of these moderated t-test methods using linear mixed-effects models, where both random variances and residual variances are smoothed under the hierarchical Bayesian framework. We will apply the proposed procedure to a real gene expression data set.

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

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