Project 1:


Harmonized data across multiple datasets

Over 13,000 persons with early Parkinson’s disease are followed over time in our data repository. These data have been cleaned, re-coded, and consolidated, giving us the ability to tap into a large data source for investigating the progression of Parkinson’s disease. This data repository continues to grow as we incorporate more study data available from the University of Rochester and through external partners.


Developed models to understand and predict the clinical progression of Parkinson’s disease

Leveraging our data repository, we have constructed statistical models to determine the impact of Parkinson’s disease on aging. For this analysis, we quantified and compared the incidence and prevalence of specific non-motor symptoms between people living with and without Parkinson’s disease (see publication).   We have also developed models to predict the clinical progression of Parkinson’s disease. Using an individual’s clinical baseline data, we are able to predict someone as having a progressive or stable trajectory of clinical progression. Our prediction models focus on the progression of ambulatory ability and cognitive deterioration. These models demonstrate people with a predicted progressive trajectory are more likely to experience clinical milestones of impaired balance, loss of independence, impaired functional ability, and cognitive impairment. These models also have the ability to help design enrichment strategies for clinical trials that would focus enrollment on those with a predicted progressive trajectory, thereby decreasing study duration and sample sizes.


Created a tool to predict clinical progression of Parkinson’s disease

An interactive tool to predict the clinical progression of Parkinson’s disease is available at  This tool interfaces with our model that predicts progression of ambulatory ability. Users enter data about their symptoms and demographics, and are able to generate a prediction of having a progressive or stable trajectory of ambulatory ability over the next several years. This tool is in the Beta stage, and will continue to be refined as we improve our prediction models and receive input from the larger Parkinson’s disease community.