TITLE:
A Flexible Joint Longitudinal-Survival Model for Analyzing Longitudinally Sampled Biomarkers
AUTHORS:
Sepehr Akhavan Masouleh, Tracy Holsclaw, Babak Shahbaba, Daniel L. Gillen
KEYWORDS:
Joint Longitudinal-Survival, Bayesian Nonparameterics, Gaussian Processes
JOURNAL NAME:
Open Journal of Statistics,
Vol.11 No.5,
October
14,
2021
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 specification of the
distribution of longitudinal responses and allows for a subject-specific
baseline hazard in the survival component. Fully joint estimation is performed
to account for uncertainty in the estimated longitudinal biomarkers that are
included in the survival model.