Abstract
In observational studies, the time origin of interest for time-to-event analysis is often unknown, such as the time of disease onset. Using the study entry as time zero often leads to misleading results. Existing approaches to estimating the time origins are commonly built on extrapolating a parametrics longitudinal model, which relies on rigid assumptions that can lead to biased inferences. In this paper, 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. Simulation studies and two real data applications demonstrate the effectiveness of this new approach.
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Dr. Wensheng Guo is a professor of biostatistics at the University of Pennsylvania Perelman School of Medicine. His research interests include functional data analysis, time series analysis, longitudinal data analysis, semiparametric models, subgroup analysis, and joint modeling of longitudinal and survival data. He is also interested in developing statistical methodology motivated by challenging real applications. He is interested in risk prediction, online prediction, modeling wearable data, ECG, EEG, imaging data, and electronic health record data. His collaborative research areas include cardiology, sleep research, aging, renal diseases, chronic pain, and neurology. He was elected as ASA fellow in 2010 and IMS fellow in 2023.