FastJM: Semi-Parametric Joint Modeling of Longitudinal and Survival Data
A joint model for large-scale, competing risks time-to-event data with singular or multiple longitudinal biomarkers, implemented with the efficient algorithms developed by Li and colleagues (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal biomarkers are modelled using a linear mixed effects model. The association between the longitudinal submodel and the survival submodel is captured through shared random effects. It allows researchers to analyze large-scale data to model biomarker trajectories, estimate their effects on event outcomes, and dynamically predict future events from patients’ past histories. A function for simulating survival and longitudinal data for multiple biomarkers is also included alongside built-in datasets.
| Version: | 1.5.3 |
| Depends: | R (≥ 3.5.0),survival, utils,MASS,statmod,magrittr |
| Imports: | Rcpp (≥ 1.0.7),dplyr,nlme,caret,timeROC,future,future.apply,rlang (≥ 0.4.11) |
| LinkingTo: | Rcpp,RcppEigen |
| Suggests: | testthat (≥ 3.0.0),spelling |
| Published: | 2025-11-08 |
| DOI: | 10.32614/CRAN.package.FastJM |
| Author: | Shanpeng Li [aut, cre], Ning Li [ctb], Emily Ouyang [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 |
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