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https://doi.org/10.1046/j.1523-1755.2002.00169.x

has been cited by the following article:

• 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.