Time-to-Event Analysis with Unknown Time Origins via Longitudinal Biomarker Registration.

ABSTRACT: 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 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 o

SUBMITTER: Wang T 

PROVIDER: S-EPMC10530746 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature