Porthole
Challenges and opportunities in statistical methods for studies of aging and dementia (306610)
*Rebecca Hubbard, University of PennsylvaniaKeywords: Alzheimer's disease, dementia, neuroimaging, trajectory models
Advancements in understanding the etiology of dementia and planning for the impact of the disease on patients, caregivers, and healthcare resources are urgently needed due to an anticipated surge in the number of cases. Ongoing evolution of our understanding of the etiology of Alzheimer’s disease has led to changes to its diagnosis to incorporate biomarkers and imaging. This creates opportunities for development of multi-dimensional trajectory models to characterize joint changes in these measures over time. Improvements in understanding of etiology also require improved methods for the analysis of neuroimaging data to account for complex features of multi-modal imaging and characterize longitudinal structural and functional changes in the brain. Our improved understanding of disease etiology and time-course must also be translated into estimates of population-level impacts including predictions of life expectancy and dementia-free life expectancy, accounting for dynamic changes in population demographics and dementia risk factors to support health policy and planning. This talk will provide an overview of the current state of the field and opportunities for statistical innovation.