DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA

Alzheimer’s Disease (AD) is a common neurodegenerative disorder impairing multiple domains. Recent AD studies, for example, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, collect multimodal data to better understand AD severity and progression. To facilitate precision medicine for high-risk individuals, it is essential to develop an AD predictive model that leverages multimodal data and provides accurate personalized predictions of dementia occurrences. In this article we propose a multivariate functional mixed model with longitudinal magnetic resonance imaging data (MFMM-LMRI) that jointly models longitudinal neurological scores, longitudinal voxelwise MRI data, and the survival outcome as dementia on-set. We model longitudinal MRI data using the joint and individual variation explained (JIVE) approach. We investigate two functional forms linking the longitudinal and survival processes. We adopt the Markov chain Monte Carlo (MCMC) method to obtain posterior samples. We establish a dynamic prediction framework that predicts longitudinal trajectories and the probability of dementia occurrence. The simulation study with various sample sizes and event rates supports the validity of the method. We apply the MFMM-LMRI to the motivating ADNI study and conclude that additional ApoE-ϵ4 alleles and a higher latent disease profile are associated with a higher risk of dementia onset. We detect a significant association between the longitudinal MRI data and the survival outcome. The instantaneous model with longitudinal MRI data has the best fitting and predictive performance.

Duke Scholars

Author Haotian Zou