Title: Robust estimators to build reliable disease trajectories from short longitudinal data

Speaker: Professor Tanya P Garcia, University of North Carolina, Chapel Hill

Abstract: Discovering therapies for neurodegenerative diseases is notoriously difficult, and made worse without accurate disease trajectories to identify when interventions will best prevent or delay irreparable damage. Modeling a disease trajectory is not easy. These diseases progress slowly over decades, and no study covers the full disease course due to time and cost constraints. To compensate, researchers model disease trajectories by piecing together short longitudinal data from patients at different disease stages. The challenge is how to piece together the data to create realistic disease trajectories. One promising way pieces together the short longitudinal data to show changes before and after major events on the disease timeline, like when disease onset occurs. This approach has helped produce realistic disease trajectories, but has shortcomings when the time of the disease event is unknown since without these times, we don’t know where to place the data on the disease timeline. To overcome this issue, researchers currently replace all unknown times with predicted times. Despite efforts to predict the time of disease events without bias using various models, the assumptions these models make often do not hold in practice and result in inaccurate predictions. This leads to an incorrect model of the disease trajectory, producing misleading conclusions about how quickly impairments change as the disease advances. We propose a series of estimators to model the disease trajectory around times of disease events without the need to predict times that are unknown. We show that our estimators produce accurate estimates of the trajectory around times of disease events even when we completely misspecified the distribution model of that time of disease event. We apply our methods to studies of Huntington disease where we model trajectories of motor impairments before and after times of major disease events, to help pinpoint when interventions will best prevent or delay irreparable damage.