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Bayesian Dirichlet Regression For Correlated Compositional Outcomes.

Abstract Number:

2382 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Eric Odoom (1), XIA WANG (1), Nanhua Zhang (2)

Institutions:

(1) University of Cincinnati, Cincinnati, OH, (2) Cincinnati Children's Hospital Medical Center, Cincinnati, OH

Co-Author(s):

Xia Wang  
University of Cincinnati
Nanhua Zhang  
Cincinnati Children's Hospital Medical Center

First Author:

Eric Odoom  
University of Cincinnati

Presenting Author:

Eric Odoom  
University of Cincinnati

Abstract Text:

It is common to observe compositional data in various fields, with a growing interest in considering compositional data as outcomes in regression settings. The motivation for this paper stems from a study investigating the impact of sleep restriction on physical activity outcomes. The compositional outcomes were measured under both short sleep and healthy sleep conditions for the same participants. To address the dependence observed in the compositional outcomes, we introduce a Mixed-Effects Dirichlet Regression (MEDR) model. This model is designed to account for correlated outcomes arising from repeated measurements on the same subject or clustering within a group. We utilize an alternative parameterization of the Dirichlet distribution, enabling the modeling of both mean and dispersion components. Our approach offers Markov Chain Monte Carlo (MCMC) tools that are easily implementable in the programming languages Stan and R. We apply the proposed MEDR model to an experimental sleep study and illustrate its performance through simulation studies.

Keywords:

Compositional data, Bayesian Dirichlet regression, Markov chain Monte Carlo, physical activity, sleep restriction.| | | | |

Sponsors:

Section on Bayesian Statistical Science

Tracks:

Applications in Life Sciences and Medicine

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