Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM

Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM

B-splines, EM algorithm, multiplicative random effects, semi-parametric models,
transformation model.

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

Cong Xu

https://www.jstatsoft.org/article/view/v093i02

Southern University of
Science and Technology

April 2020

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