JMH: Joint Model of Heterogeneous Repeated Measures and Survival Data

Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data in the presence of heterogeneous within-subject variability, proposed by Li and colleagues (2023) <arXiv:2301.06584>. The proposed method models the within-subject variability of the biomarker and associates it with the risk of the competing risks event. The time-to-event data is modeled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modeled using a mixed-effects location and scale model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.

Version: 1.0.3
Depends: R (≥ 3.5.0), survival, nlme, utils, MASS, statmod
Imports: Rcpp (≥ 1.0.7), parallel, dplyr, stats, caret, timeROC
LinkingTo: Rcpp, RcppEigen
Suggests: testthat (≥ 3.0.0), spelling
Published: 2024-02-20
Author: Shanpeng Li [aut, cre], 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
CRAN checks: JMH results

Documentation:

Reference manual: JMH.pdf

Downloads:

Package source: JMH_1.0.3.tar.gz
Windows binaries: r-devel: JMH_1.0.2.zip, r-release: JMH_1.0.2.zip, r-oldrel: JMH_1.0.3.zip
macOS binaries: r-release (arm64): JMH_1.0.3.tgz, r-oldrel (arm64): JMH_1.0.3.tgz, r-release (x86_64): JMH_1.0.3.tgz
Old sources: JMH archive

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