Semi-Parametric Joint Modeling of Longitudinal and Survival Data

Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data applying customized linear scan algorithms, proposed by Li and colleagues (2021) . The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.


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install.packages("FastJM")

1.0.1 by Shanpeng Li, 7 days ago


Browse source code at https://github.com/cran/FastJM


Authors: Shanpeng Li [aut, cre] , Ning Li [ctb] , Hong Wang [ctb] , Jin Zhou [ctb] , Hua Zhou [ctb] , Gang Li [ctb]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports Rcpp, MASS, statmod, survival, dplyr, nlme, mvtnorm

Suggests testthat, spelling

Linking to Rcpp, RcppEigen


See at CRAN