Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension.

ABSTRACT: Joint models for longitudinal biomarkers and time-to-event data are widely used in longitudinal studies. Many joint modeling approaches have been proposed to handle different types of longitudinal biomarkers and survival outcomes. However, most existing joint modeling methods cannot deal with a large number of longitudinal biomarkers simultaneously, such as the longitudinally collected gene expression profiles. In this article, we propose a new joint modeling method under the Bayesian framework, which is able to analyze longitudinal biomarkers of high dimension. Specifically, we assume that only a few unobserved latent variables are related to the survival outcome and the latent variables are inferred using a factor analysis model, which greatly reduces the dimensionality of the biomarkers

SUBMITTER: Liu M 

PROVIDER: S-EPMC7717673 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature