Joint Modeling of Multiple Gaussian Longitudinal Outcomes using Multivariate Generalized Linear Mixed Models (mGLMM)

Music City Center 

Alzheimer's disease (AD) is a growing health concern, projected to affect 13.8 million Americans by 2050. Exposures such as amyloid-β (Aβ) in cerebrospinal fluid and genetic factors like the APOE ε4 allele are not only crucial for understanding AD progression but also to understand why current treatments do not alter its progression. The Alzheimer's Disease Neuroimaging Initiative (ADNI) tracks longitudinal data on multiple cognition, and neuroimaging outcomes. However, current studies use standard methods that are limited in estimating the global effect of exposures on these outcomes accounting for their complex interrelations. We propose a mGLMM framework to jointly model multiple outcomes, providing global and individual effects of exposures. This approach accounts for different random effect specifications and error terms across all outcomes. We assessed the performance of mGLMM by simulating 1000 balanced datasets under the scenarios of 1000 patients, 5 time points, and 5 multivariate Gaussian outcomes. We applied the proposed method to examine the relationship between the exposures (APOE ε4 allele status and baseline Aβ+ status) and 8 outcomes from the motivating ADNI study, while adjusting for age, sex, and education. We show that mGLMM improves the efficiency of covariate effect estimates, while reducing Type-I error risk.