print.survfitmvjmcs

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.

Shanpeng Li

FastJM

Semi-Parametric Joint Modeling of Longitudinal and Survival Data

Ning Li

Emily Ouyang

Hong Wang

Jin Zhou

Hua Zhou

Gang Li

print.survfitmvjmcs function

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

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Shanpeng Li <a href="/link/lishanpeng0913%40ucla.edu?package=FastJM&version=1.5.3" data-mini-rdoc="FastJM::lishanpeng0913@ucla.edu">lishanpeng0913@ucla.edu</a>

Author

Print survfitmvjmcs — print.survfitmvjmcs

<dl>

<dt>x</dt>
<dd>x of class 'survfitmvjmcs'.</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.survfitmvjmcs: Print survfitmvjmcs

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