The FunSurv package provides a novel framework for the joint analysis of recurrent event and longitudinal data using a functional regression approach. This package applies functional data analysis techniques to model the time-varying effects of longitudinal predictors on recurrent event processes, addressing the complex dependence structure between the two data types.
You can install and load FunSurv from CRAN using
install.packages("FunSurv")
library(FunSurv)
You can install FunSurv from GitHub with:
## install.packages("devtools")
devtools::install_github("zifangkong/FunSurv", ref = "master")
Cite FunSurv with citation("FunSurv").
citation("FunSurv")
Online documentation is available here.
Gellar, J. E., Colantuoni, E., Needham, D. M., & Crainiceanu, C. M. (2015). Cox Regression Models with Functional Covariates for Survival Data. Statistical Modelling, 15(3), 256–278. https://doi.org/10.1177/1471082X14565526
[UNDER REVIEW] Kong, Z., Chiou, S.H., Maalouf, N.M., & Liu Y.L. (2025+) Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.
Kong, D., Ibrahim, J. G., Lee, E., & Zhu, H. (2018). FLCRM: Functional linear cox regression model. Biometrics, 74(1), 109-117.
Yao, F., Müller, H. G., & Wang, J. L. (2005). Functional linear regression analysis for longitudinal data. The Annals of Statistics, 33(6), 2873–2903.
Yao, Fang. Functional principal component analysis for longitudinal and survival data. Statistica Sinica, (2007): 965-983.
Yau, K. K. W., & McGilchrist, C. A. (1998). ML and REML estimation in survival analysis with time dependent correlated frailty. Statistics in Medicine, 17(11), 1201-1213.