Empirically Derived Trajectories to Dementia Over 15 Years of Follow-up Identified by Using Mixed Membership Models. - Lecci, Fabrizio; Junker, Brian; Kuller, Lewis H; Lopez, Oscar L; Becker, James T (Am J Epidemiol, 2015)
Alzheimer disease is the most common form of dementia in the elderly, and the complex relationships among risk factors produce highly variable natural histories from normal cognition through the prodromal stage of mild cognitive impairment (MCI) to clinical dementia. We used a novel statistical approach, mixed membership trajectory models, to capture the variety of such pathways in 652 participants in the Cardiovascular Health Study Cognition Study over 22 years of follow-up (1992-2014). We identified 3 trajectories: a "healthy" profile with a peak probability of MCI between 95 and 100 years of age and only a 50% probability of dementia by age 100; an "intermediate" profile with a peak probability of MCI between 85 and 90 years of age and progression to dementia between 90 and 95 years; and an "unhealthy" profile with a peak probability of progressing to MCI between ages 75 and 80 years and to dementia between the ages of 80 and 85 years. Hypertension, education, race, and the ϵ4 allele of the apolipoprotein E gene all affected the closeness of an individual to 1 or more of the canonical trajectories. These results provide new insights into the natural history of Alzheimer disease and evidence for a potential difference in the pathophysiology of the development of dementia.
Keywords: Age Distribution; Aged; Aged, 80 and over; Alzheimer Disease; Apolipoprotein E4; Cardiovascular Diseases; Comorbidity; Dementia; Diabetes Mellitus; Disease Progression; Female; Follow-Up Studies; Humans; Magnetic Resonance Imaging; Male; Mild Cognitive Impairment; Neuroimaging; Prevalence; Proportional Hazards Models; Risk Factors; Sex Distribution

High-dimensional longitudinal classification with the multinomial fused lasso. - Adhikari, Samrachana; Lecci, Fabrizio; Becker, James T; Junker, Brian W; Kuller, Lewis H; Lopez, Oscar L; Tibshirani, Ryan J (Stat Med, 2019)
We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an efficient algorithm for computing such estimates, based on proximal gradient descent. We apply our proposed technique to a longitudinal data set on Alzheimer's disease from the Cardiovascular Health Study Cognition Study. Using data analysis and a simulation study, we motivate and demonstrate several practical considerations such as the selection of tuning parameters and the assessment of model stability. While race, gender, vascular and heart disease, lack of caregivers, and deterioration of learning and memory are all important predictors of dementia, we also find that these risk factors become more relevant in the later stages of life.