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

TL;DR

This paper introduces a Bayesian joint modeling approach for high-dimensional longitudinal biomarkers and survival data, effectively reducing dimensionality and improving prediction accuracy in complex biomedical datasets.

Contribution

A novel Bayesian joint model that handles high-dimensional longitudinal biomarkers using latent variables inferred via factor analysis, enhancing predictive performance.

Findings

Improved prediction accuracy over existing methods

Effective dimensionality reduction for high-dimensional biomarkers

Successful application to gene expression data in pulmonary fibrosis

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 deal with 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 deal with 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 and also accounts for the high correlations among the biomarkers. Through extensive simulation studies, we show that our proposed method has improved prediction accuracy over other joint modeling methods. We illustrate the usefulness of our method on a dataset of idiopathic pulmonary fibrosis patients in which we are interested in predicting the patients' time-to-death using their gene expression profiles.