A Flexible Joint Longitudinal-Survival Modeling Framework for Incorporating Multiple Longitudinal Biomarkers

TL;DR

This paper introduces a flexible joint modeling framework that simultaneously analyzes multiple longitudinal biomarkers and survival data in hemodialysis patients, improving the understanding of their relationships.

Contribution

It presents a novel multivariate joint modeling approach that accounts for correlations among multiple longitudinal biomarkers to enhance survival analysis accuracy.

Findings

More efficient modeling of biomarker trajectories.

Improved association analysis between biomarkers and survival.

Framework applicable to complex longitudinal data.

Abstract

We are interested in survival analysis of hemodialysis patients for whom several biomarkers are recorded over time. Motivated by this challenging problem, we propose a general framework for multivariate joint longitudinal-survival modeling that can be used to examine the association between several longitudinally recorded covariates and a time-to-event endpoint. Our method allows for simultaneous modeling of longitudinal covariates by taking their correlation into account. This leads to a more efficient method for modeling their trajectories over time, and hence, it can better capture their relationship to the survival outcomes.