A QUANTILE-REGRESSION APPROACH TO BIVARIATE LONGITUDINAL JOINT MODELING

Article Type

Research Article

Publication Title

Journal of Statistical Research

Abstract

Joint modeling of longitudinal outcomes and time-to-event data has become a major research interest in the last thirty years. A joint model is useful since it helps (i) to understand the evolution of the outcome(s) of interest over time, (ii) to understand the effects of the outcomes on the time of occurrence of some event(s) of interest (e.g. death/relapse), and (iii) to study the effects of the time-varying and time-invariant predictors on the longitudinal and time-to-event process. Traditional linear mixed models are routinely used for modeling the longitudinal process. However, for non-Gaussian/skewed outcomes it is more appealing to use quantile-regression models since such models do not assume any specific probability distribution for the outcomes. In this article, we present a bivariate quantile-regression approach for jointly modeling longitudinal process and time-to-event. In a Bayesian setting we consider an Asymmetric Laplace Distribution (ALD) for modeling different quantiles of the outcomes, and a semi-parametric Proportional Hazards (PH) model for time-to-event. Model parameters are estimated using Markov chain Monte Carlo (MCMC) algorithm, and we discuss the computational complexities through several simulation studies. Our numerical studies illustrate the usefulness of our model over the other traditional models.

First Page

111

Last Page

130

DOI

10.3329/jsr.v58i1.75417

Publication Date

8-14-2024

Comments

Open Access; Hybrid Gold Open Access

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