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 <lishanpeng0913 at ucla.edu> License: GPL (≥ 3) NeedsCompilation: yes Language: en-US Materials: README CRAN checks: JMH results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=JMH to link to this page.
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