Understanding the overall progression of neurodegenerative diseases is critical to the timing
of therapeutic interventions and design of effective clinical trials. Disease progression can …

Background Joint modeling is appropriate when one wants to predict the time to an event
with covariates that are measured longitudinally and are related to the event. An underlying …

The purpose of this article is to provide a concise, broad and readily accessible overview of
longitudinal data analysis methods, aimed to be a practical guide for clinical investigators in …

Characterization of long-term disease dynamics, from disease-free to end-stage, is integral
to understanding the course of neurodegenerative diseases such as Parkinson's and …

Background Characterizing changing brain structure in neurodegeneration is fundamental
to understanding long-term effects of pathology and ultimately providing therapeutic targets …

Data-driven disease progression models are an emerging set of computational tools that
reconstruct disease timelines for long-term chronic diseases, providing unique insights into …

Objective A fundamental but still unresolved issue regarding Huntington disease (HD)
pathogenesis is whether the factors that determine age at onset are the same as those that …

We demonstrate the use of a probabilistic generative model to explore the biomarker
changes occurring as Alzheimer's disease develops and progresses. We enhanced the …

Background: The characterizing symptom of Alzheimer disease (AD) is cognitive
deterioration. While much recent work has focused on defining AD as a biological construct …

There is growing consensus that intervention and treatment of Huntington disease (HD)
should occur at the earliest stage possible. Various early-intervention methods for this fatal …