Dynamic prediction with time-dependent marker in survival analysis using supervised functional principal component analysis.

ABSTRACT: Time-varying biomarkers reflect important information on disease progression over time. Dynamic prediction for event occurrence on a real-time basis, utilizing time-varying information, is crucial in making accurate clinical decisions. Functional principal component analysis (FPCA) has been widely adopted in the literature for extracting features from time-varying biomarker trajectories. However, feature extraction via FPCA is conducted independent of the time-to-event response, which may not produce optimal results when the goal lies in prediction. With this consideration, we propose a novel supervised FPCA, where the functional principal components are determined to optimize the association between the time-varying biomarker and time-to-event outcome. The proposed framework also accommod

SUBMITTER: Shi H 

PROVIDER: S-EPMC10025984 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature