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 (2022) <doi:10.1155/2022/1362913>. 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: 1.2.0
Depends: R (≥ 3.5.0), MASS, statmod
Imports: Rcpp (≥ 1.0.7), survival, dplyr, nlme, mvtnorm, Hmisc
LinkingTo: Rcpp, RcppEigen
Suggests: testthat (≥ 3.0.0), spelling
Published: 2022-08-06
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_1.2.0.tar.gz
Windows binaries: r-devel: FastJM_1.2.0.zip, r-release: FastJM_1.2.0.zip, r-oldrel: FastJM_1.1.3.zip
macOS binaries: r-release (arm64): FastJM_1.2.0.tgz, r-oldrel (arm64): FastJM_1.1.3.tgz, r-release (x86_64): FastJM_1.2.0.tgz, r-oldrel (x86_64): FastJM_1.2.0.tgz
Old sources: FastJM archive

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