FastJM: Semi-Parametric Joint Modeling of Longitudinal and Survival Data
Implements scalable joint models for large-scale competing risks time-to-event data with one or multiple longitudinal biomarkers using the efficient algorithms developed by Li et al. (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>.
The time-to-event process is modeled using a cause-specific Cox proportional hazards model
with time-fixed covariates, while longitudinal biomarkers are modeled
using linear mixed-effects models. The association between the longitudinal
and survival processes is captured through shared random effects. The
package enables analysis of large-scale biomedical data to model biomarker
trajectories, estimate their effects on event risks, and perform dynamic
prediction of future events based on patients' longitudinal histories.
Functions for simulating survival and longitudinal data for multiple
biomarkers are included, along with built-in example datasets. The package
also supports modeling a single biomarker with heterogeneous within-subject
variability via functionality adapted from the 'JMH' package.
| Version: |
1.6.0 |
| Depends: |
R (≥ 3.5.0), survival, utils, MASS, statmod, magrittr, stats |
| Imports: |
Rcpp (≥ 1.0.7), dplyr, nlme, caret, pec, future, future.apply, rlang (≥ 0.4.11) |
| LinkingTo: |
Rcpp, RcppEigen |
| Suggests: |
testthat (≥ 3.0.0), spelling, knitr, rmarkdown |
| Published: |
2026-03-28 |
| DOI: |
10.32614/CRAN.package.FastJM |
| Author: |
Shanpeng Li [aut, cre],
Emily Ouyang [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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