A Flexible Joint Longitudinal-Survival Model for Analysis of End-Stage Renal Disease Data

TL;DR

This paper introduces a flexible joint model that links longitudinal biomarker data with survival outcomes, specifically applied to end-stage renal disease, accommodating subject-specific hazards and avoiding strict distributional assumptions.

Contribution

It presents a novel joint modeling framework that is robust, flexible, and accounts for uncertainty in longitudinal biomarker estimates in survival analysis.

Findings

Effective analysis of serum albumin and survival in renal disease

Robustness to distributional assumptions demonstrated

Subject-specific hazard modeling enhances accuracy

Abstract

We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the survival outcome of end-stage renal disease patients with time-varying serum albumin measurements. Our proposed method is robust to common parametric assumptions in that it avoids explicit distributional assumptions on longitudinal measures and allows for subject-specific baseline hazard in the survival component. Fully joint estimation is performed to account for the uncertainty in the estimated longitudinal biomarkers included in the survival model.