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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1.0.1 by Shanpeng Li, 7 days ago

Browse source code at

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