doi:10.48550/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.">

JMH: Joint Model of Heterogeneous Repeated Measures and Survival Data (original) (raw)

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) <doi:10.48550/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
DOI: 10.32614/CRAN.package.JMH
Author: Shanpeng Li [aut, cre], Jin Zhou [ctb], Hua Zhou [ctb], Gang Li [ctb]
Maintainer: Shanpeng Li
License: GPL (≥ 3)
NeedsCompilation: yes
Language: en-US
Materials: README
CRAN checks: JMH results

Documentation:

Downloads:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=JMHto link to this page.