print.survfitJMMLSM

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.

Shanpeng Li

FastJM

Semi-Parametric Joint Modeling of Longitudinal and Survival Data

Ace Mejia-Sanchez

Emily Ouyang

Gang Li

print.survfitJMMLSM function

<dl><dt>x</dt>
<dd>x of class 'survfitJMMLSM'.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Shanpeng Li <a href="/link/lishanpeng0913%40ucla.edu?package=FastJM&version=1.7.0" data-mini-rdoc="FastJM::lishanpeng0913@ucla.edu">lishanpeng0913@ucla.edu</a>

Author

Print survfitJMMLSM — print.survfitJMMLSM

<dl>

<dt>x</dt>
<dd>x of class 'survfitJMMLSM'.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

Shanpeng Li <a href='mailto:lishanpeng0913@ucla.edu'>lishanpeng0913@ucla.edu</a>

print.survfitJMMLSM: Print survfitJMMLSM

Description

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