Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang
Title: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM
Abstract: This paper is devoted to the R package JSM which performs joint statistical modeling of survival and longitudinal data. In biomedical studies it has been increasingly common to collect both baseline and longitudinal covariates along with a possibly censored survival time. Instead of analyzing the survival and longitudinal outcomes separately, joint modeling approaches have attracted substantive attention in the recent literature and have been shown to correct biases from separate modeling approaches and enhance information. Most existing approaches adopt a linear mixed effects model for the longitudinal component and the Cox proportional hazards model for the survival component. We extend the Cox model to a more general class of transformation models for the survival process, where the baseline hazard function is completely unspecified leading to semiparametric survival models. We also offer a non-parametric multiplicative random effects model for the longitudinal process in JSM in addition to the linear mixed effects model. In this paper, we present the joint modeling framework that is implemented in JSM, as well as the standard error estimation methods, and illustrate the package with two real data examples: a liver cirrhosis data and a Mayo Clinic primary biliary cirrhosis data.

Page views:: 2235. Submitted: 2016-09-08. Published: 2020-04-18.
Paper: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM     Download PDF (Downloads: 766)
Supplements:
JSM_1.0.0.tar.gz: R source package Download (Downloads: 61; 186KB)
v93i02.R: R replication code Download (Downloads: 89; 10KB)

DOI: 10.18637/jss.v093.i02

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.