Project Summary: Diseases of aging, like Alzheimer, Parkinson, and Huntington disease, are expected to affect 153 million individuals worldwide by 2050.1, 2 Treatments to prevent or slow these diseases will significantly decrease the projected impact, and modeling how disease symptoms worsen over time—the symptom trajectory—before and after a diagnosis can help evaluate if a treatment can prevent or slow a disease. Yet modeling the symptom trajectory is not easy because these diseases of aging progress slowly over decades, so studies that track symptoms often end before a diagnosis can be made. This makes time to diagnosis right-censored (i.e., a patient will reach the criteria for a diagnosis sometime after the last study visit, but exactly when is unknown), leaving researchers with the challenge of trying to model the symptom trajectory without full information about when diagnosis occurs. This challenge creates a unique statistical problem of modeling the symptom trajectory as a function of a right- censored covariate, time to diagnosis. Tackling this problem with “model-based methods,” which use models to estimate the expected time to diagnosis and then predict the symptom trajectory, are convenient, but when the model for time to diagnosis is wrong, so too is the estimated symptom trajectory. In contrast, a model-free strategy makes it easy to estimate the symptom trajectory without bias, but no model-free strategy yet exists that is simultaneously robust and predictive. This NIH supplement will provide technical training opportunities to Mr. Vazquez, a doctoral candidate of biostatistics at UNC-Chapel Hill, so that he may develop a model-free strategy that is simultaneously robust and predictive to improve both forecasting of expected symptom trajectories during clinical trials (Aim 1) and patient selection for those trials (Aim 2). The supplement will also provide mentoring and career development components designed to position Mr. Vazquez for a future career in academic research. These components include training in oral and written communication; training in grant development and writing; interacting with faculty, staff, and patients of the UNC Huntington Disease Program; presenting results at scientific meetings; and experiencing both sides of the peer review process.