FastJM: 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) <arXiv:2110.14822>. 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.

Version: 1.0.1
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.7), MASS, statmod, survival, dplyr, nlme, mvtnorm
LinkingTo: Rcpp, RcppEigen
Suggests: testthat (≥ 3.0.0), spelling
Published: 2022-01-11
Author: Shanpeng Li [aut, cre], Ning Li [ctb], Hong Wang [ctb], Jin Zhou [ctb], Hua Zhou [ctb], Gang Li [ctb]
Maintainer: Shanpeng Li <lishanpeng0913 at ucla.edu>
License: GPL (≥ 3)
NeedsCompilation: yes
Language: en-US
Materials: README NEWS
CRAN checks: FastJM results

Documentation:

Reference manual: FastJM.pdf

Downloads:

Package source: FastJM_1.0.1.tar.gz
Windows binaries: r-devel: FastJM_1.0.1.zip, r-devel-UCRT: FastJM_1.0.1.zip, r-release: FastJM_1.0.1.zip, r-oldrel: FastJM_1.0.1.zip
macOS binaries: r-release (arm64): FastJM_1.0.1.tgz, r-release (x86_64): FastJM_1.0.1.tgz, r-oldrel: FastJM_1.0.1.tgz
Old sources: FastJM archive

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