Huntington's disease (HD) is a progressive, neurodegenerative disorder that can be genetically diagnosed years before clinical symptoms onset. This presents groundbreaking opportunities to learn the overall, dynamic progression of HD which is critical to the timing of therapeutic interventions and design of effective clinical trials. Despite advancements in this area, signi?cant gaps exist about the transitional period from premanifest to mani- 7. Project Summary/Abstract fest HD, particularly how and when overt clinical symptoms and neurological deterioration develop. As part of the candidate's long-term goal to become an independent, lead expert biostatistician for neurodegenerative diseases, the overarching goal of this K01 is to acquire training in the disease-related background and quantitative analyti- cal skills to develop innovative methods that target new discoveries of HD progression. The candidate, Dr. Tanya P. Garcia, is a Huntington's Disease Society of America (HDSA) Human Biology Project Fellow (2013-2015) and has assembled a team of outstanding mentors and collaborators who will provide training to acquire the skills she lacks for an independent, biostatistically-focused, neuroscience career. Her two primary mentors are Dr. Karen Marder and Dr. Raymond J. Carroll. Dr. Marder is the Sally Kerlin Professor of Neurology at Columbia University with over 300 publications in behavioral neurology, neuroepidemiology and neurodegenerative diseases including Huntington's, Alzheimer's, Parkinson's and HIV dementia. Dr. Carroll is Distinguished Professor of Statistics at Texas A&M University with over 400 publications and 5 books in multiple statistics areas, particularly in those needed for this proposal. To conduct high-level research that ?lls signi?cant gaps about HD progression knowl- edge, Dr. Garcia proposes in-depth training (i) To learn the latest developments and challenges in clinical and neurological understanding of HD to ?ne-tune statistical methodology; (ii) To obtain pro?ciency in analysis of cor- related, longitudinal, big data; and (iii) To develop programming expertise to make the proposed methods acces- sible to neuroscience investigators in user-friendly software. Training in these areas directly support Dr. Garcia's research aims which are (i) To improve prediction of HD motor-diagnosis by modeling the time-varying effects of multiple clinical performance measures; (ii) To improve identi?cation of disease-relevant brain regions in relation to HD motor-diagnosis by modeling the spatial-temporal brain structure; and (iii) To develop the ?rst generation of a HD Progression Risk Assessment Tool (HD-PRAT). Expected research outcomes include models that support President Obama's Precision Medicine Initiative in that they adhere to ?2P's? of the NIH New Strategic Vision of the ?4P's? of Medicine: they will offer promising ways to Predict the pattern and intensity of an individual's clinical and neurological changes over time; and increase the capacity to Personalize early intervention based on these learned predictions. Having the models available in user-friendly HD-PRAT is of high
In this project, innovative statistical models are proposed that target new discoveries of Huntington's disease (HD) progression by evaluating the time-varying effects of multiple clinical performance measures and identifying disease-relevant brain regions associated with HD motor-diagnosis. Such information will facilitate the timing of future therapy interventions by enabling researchers to compute a patient's individual risk for HD motor-diagnosis based on estimated trajectories of clinical and neurodegeneration. As HD may be a protein misfolding disorder similar to Alzheimer's and Parkinson's, the methods serve as a starting point for evaluating the risk and pattern of progression for these more prevalent neurodegenerative disorders. 8. Project Narrative
| Garcia, Tanya P; Ma, Yanyuan; Marder, Karen et al. (2017) ROBUST MIXED EFFECTS MODEL FOR CLUSTERED FAILURE TIME DATA: APPLICATION TO HUNTINGTON'S DISEASE EVENT MEASURES. Ann Appl Stat 11:1085-1116 |
| Garcia, Tanya P; Marder, Karen (2017) Statistical Approaches to Longitudinal Data Analysis in Neurodegenerative Diseases: Huntington's Disease as a Model. Curr Neurol Neurosci Rep 17:14 |
| Garcia, Tanya P; Ma, Yanyuan (2017) Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models. J Econom 200:194-206 |
