The proposed R01 grant is in direct response to PAR-18-352 ?Methodology and Measurement in the Behavioral and Social Sciences (R01)?. Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that causes impairment in multiple domains (e.g., cognition, behavior, and quality of life) and progresses heterogeneously in time and across domains and individuals. No single biomarker provides sufficient information to capture the underlying severity of disease across the entire spectrum. Hence, AD studies collect data from multiple sources (e.g., clinical, neuroimaging, and genetic; multi-modal data). We propose a novel integrative modeling framework to provide statistically-principled inference, accurate personalized prediction of disease progression, and dynamic prediction update, based on new subject-specific data. This novel model development is important to identify risk and protective factors for AD and target high risk individuals, as well as to personalize the management, prognosis, and treatment selections. The overall objectives are to: (1) develop a multivariate functional mixed model (MFMM) for the integrative modeling of the longitudinal clinical data; (2) use such model to provide personalized prediction of future outcome trajectories and risks of target events; (3) advance the integrative model by incorporating the high-dimensional neuroimaging and genetic data; (4) make this methodology easily accessible via professional software development and web deployment. Our methods can be broadly applied to other clinical studies with similar multi-modal data structure.
This project uses rich multi-modal (clinical, neuroimaging, and genetic) data to develop a novel integrative modeling framework to provide statistically-principled inference, accurate personalized prediction of disease progression, and dynamic prediction update, based on new subject-specific data. This project not only facilitates the discovery and evaluation of Alzheimer?s disease (AD) variables for disease progression, but also is highly relevant for optimizing AD clinical trial design.
