Assistant Professor, Statistics
Rush Alzheimer’s Disease Center
Department of Neurological Sciences
Rush University Medical Center
The Meeting will be held via Zoom.
Meeting ID: 889 3768 4577
In observational studies, the time origin of interest for time-to-event analysis is often unknown, such as the time of disease onset. Existing approaches to estimating the time origins are commonly built on extrapolating a parametric longitudinal model, which relies on rigid assumptions that can lead to biased inferences. In this talk, we introduce a flexible semiparametric curve registration model. It assumes the longitudinal trajectories follow a flexible common shape function with person-specific disease progression pattern characterized by a random curve registration function, which is further used to model the unknown time origin as a random start time. This random time is used as a link to jointly model the longitudinal and survival data where the unknown time origins are integrated out in the joint likelihood function, which facilitates unbiased and consistent estimation. Since the disease progression pattern naturally predicts time-to-event, we further propose a new functional survival model using the registration function as a predictor of the time-to-event. The asymptotic consistency and semiparametric efficiency of the proposed models are proved. Simulation studies and two real data applications demonstrate the effectiveness of this new approach.
This group meets monthly from Noon – 1 p.m.
The meeting agenda includes a presentation, programmatic announcements, and updates from Cancer Center program leaders.