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) <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 |
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