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, e.g., 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. First, we propose the multivariate functional mixed model (MFMM) that jointly models multiple longitudinal outcomes and the time to dementia onset. We develop six functional forms to fully investigate the association between longitudinal outcomes and dementia onset. We use Bayesian methods for inference and develop a dynamic prediction framework that provides accurate personalized predictions of disease progressions based on new subject-specific data. We apply the MFMM to the ADNI study and identify the functional form with the best predictive performance. Our method is also validated by extensive simulation studies. Second, we propose an MFMM with magnetic resonance imaging data (MFMM-MRI) that simultaneously models longitudinal neurological assessments, baseline MRI data, and the survival outcome (i.e., dementia onset). We apply the MFMM-MRI to the ADNI study and identify significant associations among longitudinal outcomes, MRI data, and the risk of dementia onset. The simulation study supports the validity of the method. Third, we propose an MFMM with longitudinal MRI data (MFMM-LMRI) that jointly models longitudinal neurological scores, longitudinal voxel-wise MRI data, and the survival outcome as dementia onset. 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 and establish a dynamic prediction framework. 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-$\epsilon 4$ 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.