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Activity Number: 443 - SPEED: Statistical Methods and Applications in Medical Research, Risk Analysis, and Marketing Part 2
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 11:15 AM
Sponsor: Biopharmaceutical Section
Abstract #323831
Title: BGLAM: A Bayesian General Logistic Autoregressive Model for Correlated Binary Outcomes
Author(s): Ahmad Hakeem Abdul Wahab* and Arman Sabbaghi and Maggie O'Haire
Companies: Janssen Pharmaceuticals and Purdue University and Purdue University
Keywords: Bayesian statistics; Logistic regression; Partial autocorrelation,; epeated measures

Autoregressive processes in generalized linear mixed effects regression models are convenient for the analysis of clinical trials. However, much of the existing literature and methods for autoregressive processes on repeated binary measurements permit only one order and only one autoregressive process in the model. This limits the flexibility of the resulting generalized linear mixed effects regression model to fully capture the dynamics in the data, which can result in decreased power for testing treatment effects. We introduce the Bayesian General Logistic Autoregressive Model (BGLAM) for the analysis of repeated binary measures in clinical trials. We describe methods for selecting the order of the autoregressive process in BGLAM based on the Deviance Information Criterion (DIC) and marginal log-likelihood, and develop an importance sampling-weighted posterior predictive p-value to test for treatment effects in BGLAM. We apply our model for data collected from a clinical trial on the effects of service dogs for reducing PTSD symptoms of United States veterans.

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

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