Time-to-Event Analysis with Unknown Time Origins via Longitudinal Biomarker Registration
Conference
65th ISI World Statistics Congress 2025
Format: IPS Abstract - WSC 2025
Keywords: "statistical, functional-data-analysis
Session: IPS 917 - Harnessing the Power of Functional Data and Machine Learning in Biomedical Research
Tuesday 7 October 2 p.m. - 3:40 p.m. (Europe/Amsterdam)
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 parametric longitudinal model, which rely 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.