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Activity Number: 143 - SPEED: Bayesian Methods and Social Statistics Part 1
Type: Contributed
Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #322914
Title: Zero-Inflated Hierarchical Generalized Dirichlet Multinomial Bayesian Regression Model with Cyclic Splines for Analysis of TDP-43 on the ALS-FTD Spectrum
Author(s): Patrick Gravelle* and Roee Gutman
Companies: Brown University and Brown University
Keywords: Bayesian analysis; hierarchical modelling; splines

The TAR DNA-binding protein 43 (TDP-43) pathology characterizes the disease spectrum of amyotrophic lateral sclerosis–frontotemporal dementia (ALS-FTD). TDP-43 studies have used mouse models to assess behavioral, motor, and cognitive symptoms. Our study investigates the relationship between mouse behavior and the wild-type (WT) and TDP-43 mutated genotypes. Generally, study results are analyzed using analysis of variance (ANOVA) methods. However, common ANOVA methods are limited in their ability to capture complex relationships within the data. We propose a multivariate hierarchical Bayesian regression model with cyclic splines to model the relationship between behavioral time and genotype. Posterior predictive checks show that our proposed Bayesian model is able to describe the data accurately, unlike common ANOVA methods.

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

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